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At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system—driving over $300B in avoidable costs every year.

By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway.

We’re looking for an ML Engineer to build the production systems that train, deploy, monitor, retrain, and serve our machine-learning models reliably. You sit between software engineering, data engineering, and modeling, and you make ML work in the real world and stay working.

You will build training and inference pipelines, serve predictions through APIs and batch jobs, and stand up the monitoring that catches drift and silent degradation before they reach a patient or a partner. You will turn the models that scientists prototype into systems the company can depend on.

The ideal candidate thinks in systems rather than notebooks, knows what a model needs to become production-ready, and builds clean interfaces between data, models, and product.

Hybrid & Office Experience

We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days.

We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most.

Lunch is provided every day, and the entire team takes an hour to eat together. It's one of the ways we stay connected outside of meetings. You'll usually find us playing a board game before getting back to work.

Production ML Systems

Build and harden training pipelines.
Package models for deployment.
Serve predictions through APIs or batch jobs with reliability in mind.
Maintain feature pipelines and keep features fresh and correct.

Reliability & Observability

Monitor drift, data quality, latency, cost, and performance.
Automate retraining and validation, and design safe rollback.
Prevent training-serving skew and silent model degradation.

Collaboration & Craft

Productionize models handed off from other teams.
Build clean interfaces between data, model, and product systems.
Implement reproducibility, versioning, and model-governance artifacts..

Strong Python and software-engineering fundamentals.
Experience with ML frameworks, data pipelines, and model serving.
Experience taking models from prototype to reliable production.
Cloud infrastructure, containers, CI/CD, and orchestration.
Monitoring and observability, plus reproducibility and versioning across data, features, and models.
Comfort with security and privacy controls for sensitive data.

Background in backend engineering, data engineering, MLOps, or platform engineering.
Experience with feature stores or feature pipelines at scale.
Familiarity with healthcare data and PHI-aware systems

We aim to complete the interview process between 2–3 weeks. It will usually consist of:
- Recruiter Screen (30 minutes)
- Hiring Manager Introduction (30 minutes)
- Hands-on-Keys Technical Assessment (1 hour)
- Onsite Interview: Systems Design / Technical Case Study + Research Presentation + Behavioral Interview + Lunch with the Team (4 hours)
- References

Meaningful pre-IPO equity
Medical, dental, and vision plans 100% paid for you and your dependents
Flexible PTO + 10 paid holidays per year
401(k) with match
16-week parental leave policy for birthing parent, 8 weeks for all other parents
HSA + FSA contributions
Life insurance, plus short and long-term disability coverage
Free daily lunch in-office
Annual learning stipend

Listed by Sprinter Health for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

43 days ago

Data Scientist, Actuarial

Sprinter Health · San Francisco, California, United States

Pay

$160k–200k

Setting

Hybrid

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Data Scientist, Actuarial Location San Francisco, CA Address 394 Pacific Avenue , San Francisco, California, 94111 Employment Type Full time Location Type Hybrid Department Sprinter Health Engineering Compensation SF Bay Area Estimated Base Salary $160K – $200K • Offers Equity Overview Application About Sprinter Health: At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system—driving over $300B in avoidable costs every year. By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway. About the Role We’re looking for a Data Scientist with an actuarial focus to quantify the long-term economic value of what Sprinter does. When we tell a health plan that finding and treating disease in the home lowers their cost of care over time, someone has to turn that claim into numbers the plan’s actuaries will trust. That is this role. You will build the models yourself: total-cost-of-care and per-member-per-month (PMPM) projections from claims, medical-loss-ratio (MLR) impact, and a defensible account of how Sprinter’s interventions change cost and risk over a multi-year horizon. You will package those outputs so a customer’s actuarial team can plug them straight into their pricing, reserving, and bid work. This is a hands-on, applied role, not an advisory one — we would rather you build the table than write a memo about it. The ideal candidate is a quantitative scientist who thinks like an actuary and is comfortable talking actuary-to-actuary with a payer’s medical-economics team. Formal actuarial credentials are welcome but not required; we care that you can do the work and defend it. This is a first-of-function role; you will define what good looks like. Hybrid & Office Experience We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days. We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most. Lunch is provided every day, and the entire team takes an hour to eat together. It's one of the ways we stay connected outside of meetings. You'll usually find us playing a board game before getting back to work. What you will do: Actuarial & Economic Modeling Build total-cost-of-care, PMPM, and MLR models from claims data to quantify the long-term impact of Sprinter’s programs. Project how interventions change cost, utilization, and risk over multi-year horizons, and quantify the uncertainty around those projections. Produce model outputs and tables that a payer’s actuaries can plug directly into their pricing, reserving, and bid work. Payer Credibility & Commercial Support Represent Sprinter in MLR and medical-economics conversations with health plans; go toe-to-toe with their actuaries. Turn analysis into value narratives that quality, risk, and finance teams can act on. Help the commercial team price and sell Sprinter’s impact on an actuarial basis. Measurement & Rigor Define the yardstick for whether an intervention actually changed cost and outcomes, not just whether it correlated with them. Partner with Data Science on the causal and experimental design behind those measurements. Bring an honest view of the line between value we can prove and value we can only assert. What you have done: Deep experience building actuarial or health-economic models from administrative claims: total cost of care, PMPM, utilization, trend, and risk. Command of the methods payers price on — MLR, risk adjustment, and multi-year projection — and the judgment to know their limits. Strong SQL and Python or R, with the ability to build and own your models end to end. Ability to hold your own with actuaries and medical-economics teams, and to explain the analysis to non-technical stakeholders. Honesty about causal inference — what a given design can and cannot claim. What gives you an edge: Actuarial credentials (ASA, FSA, MAAA, or actuarial exam progress) — welcome but not required. Payer-side, value-based-care, or risk-bearing experience; familiarity with Medicare Advantage, Stars, and risk adjustment. Experience producing analysis that a customer or partner built into their own pricing or reserving. Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow. Interview Process: We aim to complete the interview process between 2–3 weeks. It will usually consist of: Recruiter Screen (30 minutes) Hiring Manager Introduction (30 minutes) Hands-on-Keys Technical Assessment (1 hour) Onsite Interview: Systems Design / Technical Case Study + Research Presentation + Behavioral Interview + Lunch with the Team (4 hours) References What we offer: Meaningful pre-IPO equity Medical, dental, and vision plans 100% paid for you and your dependents Flexible PTO + 10 paid holidays per year 401(k) with match 16-week parental leave policy for birthing parent, 8 weeks for all other parents HSA + FSA contributions Life insurance, plus short and long-term disability coverage Free daily lunch in-office Annual learning stipend Apply for this Job Powered by Privacy PolicySecurityVulnerability Disclosure

Listed by Sprinter Health for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

50 days ago

Machine Learning Engineer (Staff)

Sprinter Health · San Francisco, California, United States

Pay

$220k–270k

Setting

Hybrid

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Machine Learning Engineer (Staff) Location San Francisco, CA; Menlo Park, CA Address 394 Pacific Avenue , San Francisco, California, 94111 Employment Type Full time Location Type Hybrid Department Sprinter Health Engineering Compensation SF Bay Area Estimated Base Salary $220K – $270K • Offers Equity Overview Application Staff Machine Learning Engineer About Sprinter Health At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system, driving over $300B in avoidable costs every year. By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators has raised over $125M from investors like a16z, General Catalyst, GV, and Accel and enjoys multi-year runway. About the Role We’re looking for a Staff Machine Learning Engineer to be Sprinter’s first dedicated ML engineering hire and build the production systems that train, deploy, monitor, retrain, and serve machine learning models across the company. This is a founding, first-of-function role. You will define the blueprint for how ML moves from prototype to production at Sprinter, including our training and inference pipelines, serving patterns, feature workflows, monitoring, validation, retraining, and model governance practices. You’ll work closely with engineering, data, product, operations, and applied science teams to turn models into reliable systems the company can depend on. That includes serving predictions through APIs and batch jobs, building clean interfaces between data and product systems, and implementing the observability needed to catch drift, data quality issues, latency problems, cost regressions, and silent model degradation before they impact patients or operations. Just as importantly, you’ll make the foundational calls that every future model and ML engineer will build on: build versus buy, serving architecture, feature paradigms, deployment standards, monitoring expectations, and the guardrails that allow us to move quickly without creating fragile systems. This role is ideal for a staff-level, hands-on engineer who thinks in systems, has built ML infrastructure from the ground up, and knows how to right-size solutions for a rapidly growing startup. You should be someone who empowers the teams around you, accelerates time to deployment, and knows what a model needs to be truly production-ready. As the function grows, you will have the opportunity to shape the team, define the technical bar, and help build the ML engineering foundation for Sprinter. Office Location We are a hybrid company based in the Bay Area with offices in both San Francisco and Menlo Park. We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days. We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most. Lunch is provided every day, and the entire team takes an hour to eat together. It’s one of the ways we stay connected outside of meetings. You’ll usually find us playing a board game before getting back to work. What you will do Build and lead Sprinter’s ML engineering function as the company’s first dedicated ML engineering hire Define Sprinter’s ML platform and deployment paradigm across training, serving, features, monitoring, retraining, and governance Make foundational build-versus-buy, architecture, tooling, and platform decisions that future models and engineers will build on Design and build production training and inference pipelines that are reliable, observable, and maintainable Package models for deployment and serve predictions through APIs, batch jobs, or other production workflows Build clean interfaces between data systems, models, and product systems so ML can be consumed safely and reliably Maintain feature pipelines and ensure features remain fresh, correct, and consistent between training and serving Implement monitoring for model performance, drift, data quality, latency, cost, reliability, and production behavior Prevent training-serving skew, silent degradation, and model regressions before they become production issues Automate retraining, validation, deployment, rollback, and other production ML workflows where appropriate Establish reproducibility, versioning, model governance, and operational readiness practices as company defaults Partner with engineering, data platform, product, operations, and applied science teams to productionize models and improve handoffs Write design docs, define technical standards, and bring the broader engineering organization along on key ML infrastructure decisions Set the technical bar for ML engineering by helping interview, mentor, and eventually hire engineers who follow What you have done Spent 8+ years building production software, data systems, ML systems, platform infrastructure, or related technical systems Built and owned ML systems in production across training, serving, features, monitoring, and deployment Taken models from prototype or research stage into reliable, production-grade systems Built or meaningfully scaled ML infrastructure, MLOps platforms, model-serving systems, feature pipelines, or related infrastructure Designed systems that other engineers, data scientists, analysts, or product teams rely on Made architectural decisions around ML platform design, serving patterns, feature infrastructure, build versus buy, and operational standards Worked with cloud infrastructure, containers, CI/CD, orchestration, data pipelines, and production deployment workflows Built monitoring, observability, validation, or alerting for ML systems, data systems, or high-reliability production services Created reproducible workflows across data, features, models, training runs, deployments, or experiments Partnered closely with data science, applied science, data platform, product, operations, or backend engineering teams Operated in ambiguous environments where there was no existing playbook and technical decisions had a long half-life Balanced speed, simplicity, reliability, privacy, and long-term maintainability in production systems What gives you an edge You’ve been an early ML engineer, founding ML engineer, or first ML infrastructure hire at a startup You’ve built ML infrastructure in a high-growth or operationally complex environment You have depth in large-scale model serving, feature infrastructure, LLM infrastructure, or real-time inference systems You have a background in backend engineering, data engineering, MLOps, platform engineering, or infrastructure engineering You have experience with feature stores, feature pipelines, or production data systems at scale You’ve helped interview, hire, mentor, or set the technical bar for ML engineers, platform engineers, or data engineers You’ve worked with healthcare data, PHI, HIPAA-aware systems, or other sensitive data environments You have experience with security, privacy, governance, or compliance considerations for production ML systems What makes you successful You decide what the pattern should be and bring the rest of the organization along You reach for the simplest system that works, adding complexity only when the value justifies it You know what it takes to make a model production-ready and can communicate those requirements clearly You are an accelerator for applied science, data, product, and engineering teams, not a gatekeeper You build interfaces that make models easy to consume and hard to misuse You prevent silent degradation before it becomes an incident You create standards that help future engineers move faster You raise the technical bar for everyone who joins the function after you Day to Day In this role, you might spend your time: Deciding what Sprinter’s serving and feature paradigms should be and writing the design docs behind those decisions Hardening a training pipeline or batch-inference workflow Productionizing a model handed off from another team Debugging a model-serving issue or production data quality problem Reviewing feature freshness, model performance, drift, latency, or cost Building validation and rollback workflows for model deployments Partnering with product and operations teams to understand how model behavior impacts real-world workflows Interviewing a candidate, mentoring an engineer, or setting a new technical standard for the ML engineering function The Interview Process We aim to complete the interview process within 2–3 weeks. It will usually consist of: Recruiter Screen: Background fit, motivation, and compensation alignment Hiring Manager Interview: Technical experience, first-of-function fit, and ML infrastructure depth Hands-on Technical Assessment: Practical ML engineering, production systems, and implementation ability Onsite Interview: Systems design, technical case study, behavioral interview, and lunch with the team References: Validation of performance, judgment, and working style What we offer Meaningful pre-IPO equity Medical, dental, and vision plans 100% paid for you and your dependents Flexible PTO + 10 paid holidays per year 401(k) with match 16-week parental leave policy for birthing parent, 8 weeks for all other parents HSA + FSA contributions Life insurance, plus short and long-term disability coverage Free daily lunch in-office Annual learning stipend Relocation assistance Equal Opportunity Statement Sprinter Health is an equal opportunity employer. We value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected classes. Beware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers. If you are applying for a job, you can confirm the legitimacy of a job posting by viewing current open roles on our official Sprinter Health Careers website. All legitimate job postings will require an application to be made directly on our official Sprinter Health Careers website. Job-related communications will only be sent from email addresses ending in @sprinterhealth.com. Please ensure that you’re only replying to emails that end with @sprinterhealth.com. Apply for this Job Powered by Privacy PolicySecurityVulnerability Disclosure

Listed by Sprinter Health for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

50 days ago

Analytics Engineer (Senior)

Sprinter Health · San Francisco, California, United States

Pay

$165k–215k

Setting

Hybrid

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Analytics Engineer (Senior) Location San Francisco, CA; Menlo Park, CA Address 394 Pacific Avenue , San Francisco, California, 94111 Employment Type Full time Location Type Hybrid Department Sprinter Health Engineering Compensation SF Bay Area Estimated Base Salary $165K – $215K • Offers Equity Overview Application Analytics Engineer About Sprinter Health At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system, driving over $300B in avoidable costs every year. By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators has raised over $125M from investors like a16z, General Catalyst, GV, and Accel and enjoys multi-year runway. About the Role We’re looking for an Analytics Engineer to build the trusted data layer that analysts, data scientists, operations, finance, product, and our payer customers depend on. At Sprinter, data is central to how we operate, measure performance, serve patients, and support our health plan partners. This role will own the canonical models, metric definitions, transformation logic, documentation, and tests that make our data reliable and reusable across the company. You’ll help define what each table, field, and metric means, then build the infrastructure that ensures those definitions are consistently applied. That includes modeling data in dbt or equivalent tooling, creating reporting-ready tables, improving lineage and documentation, reconciling metrics across teams, and helping prevent the kind of data drift and metric chaos that slows companies down as they scale. This role is ideal for someone who treats metric definitions as product artifacts, thinks in contracts and tests, and cares deeply about making data trustworthy for both internal users and external customers. Office Location We are a hybrid company based in the Bay Area with offices in both San Francisco and Menlo Park. We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days. We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most. Lunch is provided every day, and the entire team takes an hour to eat together. It’s one of the ways we stay connected outside of meetings. You’ll usually find us playing a board game before getting back to work. What you will do Build canonical data models that create a shared source of truth across the company Define and maintain core business, operational, financial, product, and customer-facing metrics Model data in dbt or equivalent transformation tooling so dashboards, self-serve analytics, and customer reports pull from trusted tables Write tests, documentation, and data quality checks that catch issues before they reach users Create clear definitions for tables, fields, and metrics so teams understand what the data means and when to use it Reconcile metric definitions across internal teams, external reporting needs, and payer customer expectations Trace data lineage and debug dashboards, reports, or tables that change unexpectedly Partner with analysts, data scientists, operations, finance, product, engineering, and customer-facing teams to understand data needs and translate them into reliable models Help build reusable reporting frameworks that make onboarding new payers faster and less manual Partner with the data platform team to evolve warehouse tables, improve data architecture, and strengthen data contracts Improve warehouse cost, performance, and maintainability Support PHI-aware data access patterns and help ensure sensitive healthcare data is modeled and used responsibly What you have done Built analytics engineering, business intelligence, or data modeling systems in a production cloud warehouse environment Written expert-level SQL and designed data models that support reporting, analysis, and decision-making Worked with dbt or an equivalent transformation framework Built tested, documented, reusable data models rather than one-off queries Defined, maintained, or reconciled business-critical metrics across teams Partnered with analysts, data scientists, operators, finance teams, product teams, or customer-facing stakeholders Debugged data quality issues, dashboard changes, metric discrepancies, and lineage problems Worked with cloud data warehouses such as BigQuery, Snowflake, Redshift, Databricks SQL, or similar Balanced speed, correctness, usability, and maintainability when building data assets Communicated clearly with technical and non-technical stakeholders about what data means and how it should be used What gives you an edge You have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets You’ve worked with PHI, HIPAA-aware data access patterns, or other sensitive regulated data You have experience building customer-facing reporting, embedded analytics, or multi-tenant data models You’ve worked with row-level security, access controls, or governed self-serve analytics You have experience using Python for analysis, scripting, data validation, or automation You’ve helped establish a semantic layer, metrics layer, or company-wide source of truth You’ve built data models in a high-growth startup or operationally complex environment You have experience improving warehouse performance, cost, and query efficiency What makes you successful You treat a metric definition as a product artifact, not a Slack thread You make data trustworthy, reusable, and easy to understand You prevent metric chaos by building clear definitions, tests, and documentation You build so that a fix in one place does not require five copy-paste edits elsewhere You understand that internal users and external customers both need data they can trust You care about the usability of the data model, not just whether the pipeline runs You can explain data discrepancies clearly and drive teams toward shared definitions You build foundations that help the company move faster with more confidence Day to Day In this role, you might spend your time: Building or refactoring dbt models Adding tests to core tables Defining canonical fields and documenting how they should be used Reviewing metric definitions and reconciling them across teams Debugging a dashboard, report, or customer-facing metric that changed unexpectedly Tracing lineage from source systems through warehouse models to downstream reports Partnering with analysts, operators, finance, product, or customer-facing teams on reporting needs Improving warehouse performance, cost, and maintainability Designing reusable reporting structures that make new payer launches easier The Interview Process We aim to complete the interview process within 2–3 weeks. It will usually consist of: Recruiter Screen: Background fit, motivation, and compensation alignment Hiring Manager Interview: Analytics engineering experience, data modeling depth, and stakeholder partnership Hands-on Technical Assessment: SQL, data modeling, metric design, and practical analytics engineering judgment Onsite Interview: Technical case study, systems/data modeling discussion, behavioral interview, and lunch with the team References: Validation of performance, judgment, and working style What we offer Meaningful pre-IPO equity Medical, dental, and vision plans 100% paid for you and your dependents Flexible PTO + 10 paid holidays per year 401(k) with match 16-week parental leave policy for birthing parent, 8 weeks for all other parents HSA + FSA contributions Life insurance, plus short and long-term disability coverage Free daily lunch in-office Annual learning stipend Relocation assistance Equal Opportunity Statement Sprinter Health is an equal opportunity employer. We value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected classes. Beware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers. If you are applying for a job, you can confirm the legitimacy of a job posting by viewing current open roles on our official Sprinter Health Careers website. All legitimate job postings will require an application to be made directly on our official Sprinter Health Careers website. Job-related communications will only be sent from email addresses ending in @sprinterhealth.com. Please ensure that you’re only replying to emails that end with @sprinterhealth.com. Apply for this Job Powered by Privacy PolicySecurityVulnerability Disclosure

Listed by Sprinter Health for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

50 days ago

Applied Scientist, AI

Sprinter Health · San Francisco, California, United States

Pay

$180k–260k

Setting

Hybrid

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Applied Scientist, AI Location San Francisco, CA Address 394 Pacific Avenue , San Francisco, California, 94111 Employment Type Full time Location Type Hybrid Department Sprinter Health Engineering Compensation SF Bay Area Estimated Base Salary $180K – $260K • Offers Equity Overview Application About Sprinter Health At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system, driving over $300B in avoidable costs every year. By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators has raised over $125M from investors like a16z, General Catalyst, GV, and Accel and enjoys multi-year runway. About the Role We’re looking for an Applied Scientist, AI to turn messy, high-stakes healthcare problems into machine learning models and AI systems that improve access to care and help Sprinter operate more effectively. This role sits at the intersection of research, product, engineering, and clinical operations. You’ll take ambiguous product and operational problems and turn them into well-scoped prediction, ranking, optimization, NLP, or LLM-based tasks. You’ll build strong baselines, design honest evaluations, run careful error analysis, and iterate toward models that can improve real-world outcomes. The right person for this role combines scientific rigor with a deployment-oriented mindset. You should care deeply about evaluation, leakage, bias, confounding, and whether offline results actually translate into production impact. You should also be able to partner closely with ML engineering to productionize models, work with clinicians and subject-matter experts to validate assumptions, and explain model behavior, uncertainty, and limitations clearly to product and leadership. This role is ideal for a scientist-engineer who can move fluidly between data exploration, modeling, experimentation, error analysis, stakeholder partnership, and production handoff. Office Location We are a hybrid company based in the Bay Area with offices in both San Francisco and Menlo Park. We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days. We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most. Lunch is provided every day, and the entire team takes an hour to eat together. It’s one of the ways we stay connected outside of meetings. You’ll usually find us playing a board game before getting back to work. What you will do Turn ambiguous healthcare, product, and operational problems into well-posed ML, AI, ranking, optimization, NLP, or LLM-based tasks Build strong baselines and improve on them efficiently using the right modeling approach for the problem Develop models across traditional ML, deep learning, NLP, and LLM-based approaches where appropriate Design offline and online evaluations that are honest, measurable, and predictive of real-world impact Choose metrics suited to imbalanced, delayed, noisy, and partially observed healthcare outcomes Run careful error analysis and use it to improve model quality, product fit, and operational usefulness Identify label leakage, selection bias, confounding, and other data artifacts before they reach production Explore messy real-world data, assess label quality, and determine whether a problem is ready for modeling Partner with ML engineering to productionize models reliably and define what production-readiness requires Work with clinical stakeholders and subject-matter experts to validate assumptions, review model errors, and understand edge cases Explain model tradeoffs, uncertainty, limitations, and expected impact clearly to product, operations, clinical, and leadership teams Write experiment docs, summarize findings, and help teams make informed decisions about when and how to deploy AI systems Pressure-test whether results are real, robust, and useful before recommending production use What you have done Built, evaluated, and iterated on machine learning or AI models for real-world use cases Turned ambiguous business, product, clinical, or operational problems into measurable modeling tasks Designed rigorous offline evaluations, experiments, or analyses that informed production or product decisions Worked with messy real-world datasets where labels, outcomes, and causal relationships are imperfect Used statistical reasoning, experimental design, and error analysis to understand model performance Built models using Python and standard ML or AI tooling such as PyTorch, scikit-learn, NumPy, pandas, Polars, Hugging Face, Matplotlib, or similar Compared modeling approaches and made pragmatic decisions about when to use traditional ML, LLMs, heuristics, or simpler baselines Communicated model performance, limitations, tradeoffs, and uncertainty to technical and non-technical stakeholders Partnered with engineering, product, data, operations, clinical, or domain experts to move models closer to production impact Operated with enough engineering depth to run experiments end to end and self-serve deployments or production handoffs when needed Used AI coding assistants such as Claude Code, Cursor, or similar tools as part of your development workflow What gives you an edge You have an MS or PhD in computer science, statistics, machine learning, applied math, operations research, biomedical informatics, epidemiology, or a related quantitative field You have exceptional applied experience that substitutes for formal graduate training You have depth in LLMs, ranking, NLP, uncertainty quantification, causal inference, optimization, or healthcare AI You’ve shipped models that reached production and had measurable real-world impact You’ve worked with healthcare data such as claims, EHR, clinical notes, scheduling, utilization, quality, risk, or patient engagement data You have experience working with PHI, HIPAA-aware systems, or other sensitive regulated data You know when traditional ML approaches are likely to outperform LLMs, and when LLMs are the right tool You have experience collaborating with clinicians, clinical operations teams, or other high-stakes domain experts You’ve worked in a startup or fast-moving applied environment where ambiguity, speed, and rigor all mattered What makes you successful You understand how ML models work under the hood and can explain them clearly to non-technical stakeholders You focus relentlessly on impact and know that the simplest model is often the best one You treat evaluation as one of the most important parts of model development You notice when a metric is misleading, incomplete, or disconnected from real-world outcomes You catch leakage, bias, and confounding that others miss You move fluidly between modeling, error analysis, stakeholder partnership, and production handoff You can hand a model to engineering and explain its limits to a clinician with equal clarity You are comfortable with ambiguity and can adapt modeling approaches to problems that do not come with a playbook You balance scientific rigor with the practical need to ship useful systems Day to Day In this role, you might spend your time: Exploring data and labels for a new healthcare or operational problem Turning an ambiguous product question into a measurable modeling task Building and comparing models, then running error analysis Reviewing misclassified or low-confidence cases with a clinical subject-matter expert Designing an offline evaluation that is more likely to predict online or real-world success Partnering with ML engineering to prepare a model for deployment Writing an experiment doc and presenting findings to product and leadership Pressure-testing whether a result is real or an artifact of the data Comparing a simple baseline, traditional ML model, and LLM-based approach to determine what is most useful Investigating why model performance differs across populations, workflows, labels, or operational contexts The Interview Process We aim to complete the interview process within 2–3 weeks. It will usually consist of: Recruiter Screen: Background fit, motivation, and compensation alignment Hiring Manager Interview: Applied science experience, modeling depth, and healthcare/product orientation Hands-on Technical Assessment: Practical modeling, evaluation, error analysis, and scientific judgment Onsite Interview: Technical case study, research or project presentation, behavioral interview, and lunch with the team References: Validation of performance, judgment, and working style What we offer Meaningful pre-IPO equity Medical, dental, and vision plans 100% paid for you and your dependents Flexible PTO + 10 paid holidays per year 401(k) with match 16-week parental leave policy for birthing parent, 8 weeks for all other parents HSA + FSA contributions Life insurance, plus short and long-term disability coverage Free daily lunch in-office Annual learning stipend Relocation assistance Equal Opportunity Statement Sprinter Health is an equal opportunity employer. We value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected classes. Beware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers. If you are applying for a job, you can confirm the legitimacy of a job posting by viewing current open roles on our official Sprinter Health Careers website. All legitimate job postings will require an application to be made directly on our official Sprinter Health Careers website. Job-related communications will only be sent from email addresses ending in @sprinterhealth.com. Please ensure that you’re only replying to emails that end with @sprinterhealth.com. Apply for this Job Powered by Privacy PolicySecurityVulnerability Disclosure

Listed by Sprinter Health for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

56 days ago

Product Engineering Team - Software Engineer (Mid-Level)

Sprinter Health · Menlo Park, California, United States

Pay

$165k–200k

Setting

Hybrid

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We’re looking for a Backend or Full-Stack Software Engineer (2-3 years experience) who wants to build the core systems that power last-mile healthcare delivery. At Sprinter, you’ll work on products that blend logistics, patient experience, safety, and medical operations—building and scaling backend services, data flows, integrations, and in-product experiences that directly determine whether patients get care. From routing clinicians and powering booking flows to integrating medical devices and preventing clinical errors, you’ll own complex, high-impact systems from 0→1. If you want to solve real problems with code and ship product that changes access to care, this is that role.

Hybrid & Office Experience

We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days.

What you will you do:

Design and build backend services and APIs that power patient booking, clinician routing, logistics, and device integrations
Ship full-stack product features end to end, from data modeling and business logic to UI and user flows (if full-stack)
Solve complex operational challenges like scheduling, dispatch, safety checks, and error prevention at scale
Integrate with external health systems, telemedicine platforms, and medical devices in a reliable, secure way
Work closely with product, data, ops, and clinical teams to turn real-world problems into shipped software
Own projects from 0→1, make architectural decisions, and help evolve our engineering practices as we scale

What you have done:

Spent 2+ years building and scaling backend or full-stack systems in production
Designed APIs, data models, and services that power real user-facing products
Led projects or pods end to end — from architecture and planning to delivery and iteration
Mentored junior and mid-level engineers through code reviews, pairing, and technical guidance
Shipped features independently and in collaboration with product, data, ops, or design partners
Built in environments where speed, reliability, and ambiguity coexist — startups, high-growth teams, or 0→1 products
Made technical decisions that balanced execution speed, scalability, and long-term maintainability

What gives you an edge:

You’ve built or scaled products in health tech, logistics, or other operationally complex environments
You understand supply/demand dynamics — forecasting, routing, scheduling, or inventory management
You’ve scaled backend systems to support rapid growth, high-volume data, and evolving business needs
You’ve worked in mid- or growth-stage startups (Series A–C) where speed and ambiguity were the norm
You’re comfortable working with data platforms, pipelines, or large-scale data processing
You know your way around regulated environments — HIPAA, security, and privacy best practices

The Interview Process

We aim to complete the interview process between 2-3 weeks. It will usually consist of:
Recruiter Screen (30-Minutes)
Technical Assessment (45-Minutes)
Hiring Manager Introduction (30-Minutes)
Onsite Interview: Systems Design + Behavioral Interview + Lunch with the Team (3-hours)
References

What we offer

Meaningful pre-IPO equity
Medical, dental, and vision plans 100% paid for you and your dependents
Flexible PTO + 10 paid holidays per year
401(k) with match
16-week parental leave policy for birthing parent, 8 weeks for all other parents
HSA + FSA contributions
Life insurance, plus short and long-term disability coverage
Free daily lunch in-office
Annual learning stipend

Our Technology Stack

Serverless AWS (AppSync, DynamoDB, Lambda, Amplify, CloudFormation, Node)
React Native, React Native for Web
GraphQL
Typescript
Javascript
Node.js

Sprinter Health is an equal opportunity employer. We value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or other protected classes.

Beware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers.

If you are applying for a job, you can confirm the legitimacy of a job posting by viewing current open roles here. All legitimate job postings will require an application to be made directly on our official Sprinter Health Careers website. Job-related communications will only be sent from email addresses ending in @sprinterhealth.com. Please ensure that you’re only replying to emails that end with @sprinterhealth.com.

Listed by Sprinter Health for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

56 days ago

Product Engineering Team - Software Engineer (Senior)

Sprinter Health · Menlo Park, California, United States

Pay

$180k–225k

Setting

Hybrid

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Product Engineering Team - Software Engineer (Senior) Location Menlo Park, CA Address 4600 Bohannon Dr, Ste 100, Menlo Park, California, 94025 Employment Type Full time Location Type Hybrid Department Sprinter Health Engineering Compensation $180K – $225K • Offers Equity Overview Application About Sprinter Health: At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system—driving over $300B in avoidable costs every year. By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway. About the Role We’re looking for a Senior Backend or Full-Stack Software Engineer (5+ years experience) who wants to build the core systems that power last-mile healthcare delivery. At Sprinter, you’ll work on products that blend logistics, patient experience, safety, and medical operations—building and scaling backend services, data flows, integrations, and in-product experiences that directly determine whether patients get care. From routing clinicians and powering booking flows to integrating medical devices and preventing clinical errors, you’ll own complex, high-impact systems from 0→1. If you want to solve real problems with code and ship product that changes access to care, this is that role. Hybrid & Office Experience We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days. We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most. Lunch is provided every day, and the entire team takes an hour to eat together. It's one of the ways we stay connected outside of meetings. You'll usually find us playing a board game before getting back to work. What you will you do: Design and build backend services and APIs that power patient booking, clinician routing, logistics, and device integrations Ship full-stack product features end to end, from data modeling and business logic to UI and user flows (if full-stack) Solve complex operational challenges like scheduling, dispatch, safety checks, and error prevention at scale Integrate with external health systems, telemedicine platforms, and medical devices in a reliable, secure way Work closely with product, data, ops, and clinical teams to turn real-world problems into shipped software Own projects from 0→1, make architectural decisions, and help evolve our engineering practices as we scale What you have done: Spent 5+ years building and scaling backend or full-stack systems in production Designed APIs, data models, and services that power real user-facing products Led projects or pods end to end — from architecture and planning to delivery and iteration Mentored junior and mid-level engineers through code reviews, pairing, and technical guidance Shipped features independently and in collaboration with product, data, ops, or design partners Built in environments where speed, reliability, and ambiguity coexist — startups, high-growth teams, or 0→1 products Made technical decisions that balanced execution speed, scalability, and long-term maintainability What gives you an edge: You’ve built or scaled products in health tech, logistics, or other operationally complex environments You understand supply/demand dynamics — forecasting, routing, scheduling, or inventory management You’ve scaled backend systems to support rapid growth, high-volume data, and evolving business needs You’ve worked in mid- or growth-stage startups (Series A–C) where speed and ambiguity were the norm You’re comfortable working with data platforms, pipelines, or large-scale data processing You know your way around regulated environments — HIPAA, security, and privacy best practices The Interview Process We aim to complete the interview process between 2-3 weeks. It will usually consist of: Recruiter Screen (30-Minutes) Technical Assessment (45-Minutes) Hiring Manager Introduction (30-Minutes) Onsite Interview: Systems Design + Behavioral Interview + Lunch with the Team (3-hours) References What we offer Meaningful pre-IPO equity Medical, dental, and vision plans 100% paid for you and your dependents Flexible PTO + 10 paid holidays per year 401(k) with match 16-week parental leave policy for birthing parent, 8 weeks for all other parents HSA + FSA contributions Life insurance, plus short and long-term disability coverage Free daily lunch in-office Annual learning stipend Our Technology Stack Serverless AWS (AppSync, DynamoDB, Lambda, Amplify, CloudFormation, Node) React Native, React Native for Web GraphQL Typescript Javascript Node.js Sprinter Health is an equal opportunity employer. We value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or other protected classes. Beware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers. If you are applying for a job, you can confirm the legitimacy of a job posting by viewing current open roles here. All legitimate job postings will require an application to be made directly on our official Sprinter Health Careers website. Job-related communications will only be sent from email addresses ending in @sprinterhealth.com. Please ensure that you’re only replying to emails that end with @sprinterhealth.com. Apply for this Job Powered by Privacy PolicySecurityVulnerability Disclosure

Listed by Sprinter Health for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

63 days ago

Product Engineering - Engineering Manager / Sr. Engineering Manager

Sprinter Health · San Francisco, California, United States

Pay

$235k–275k

Setting

Hybrid

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Product Engineering - Engineering Manager / Sr. Engineering Manager Location San Francisco, CA Address 394 Pacific Avenue , San Francisco, California, 94111 Employment Type Full time Location Type Hybrid Department Sprinter Health Engineering Compensation Estimated Base Salary $235K – $275K • Offers Equity Overview Application About Sprinter Health: At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system—driving over $300B in avoidable costs every year. By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway. About the Role Our engineering team has scaled from 12 to 40+ engineers in the past year, and we’re still growing fast. We’re looking for experienced mid- to senior-level Engineering Managers to help us navigate this next phase. You’ve led teams of around 10 engineers at varying levels, and you know how to build for scale—hiring, career development, mentorship, org design, and beyond. We have multiple teams tackling a wide range of technical challenges, and we’ll work with you to place you where your interests and strengths align. Hybrid & Office Experience We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days. We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most. Lunch is provided every day, and the entire team takes an hour to eat together. It's one of the ways we stay connected outside of meetings. You'll usually find us playing a board game before getting back to work. What you will do: Lead and grow a team of 8–12 engineers — recruiting, coaching, and guiding performance and career development. Stay close to the work with a full-stack engineering background, providing technical direction and unblockers when needed. Drive fast execution without compromising technical quality, especially in a high-growth, high-ambiguity environment. Partner closely with Product, Operations, and Data to deliver measurable impact for the business and our users. Shape team structure, culture, and processes as we continue to scale. What you have done: 3+ years of experience managing and growing engineering teams (8+ engineers), including hiring, mentoring, and performance management 5+ years of hands-on software engineering experience, with a focus on full-stack development Proven ability to balance fast-paced execution with technical excellence, particularly in startup or high-growth environments Experience working cross-functionally with product, operations, and data teams to drive business and customer impact What gives you an edge: Experience in health tech, healthcare operations, or logistics (supply/demand, forecasting, inventory, routing, scheduling) Experience scaling backend systems to support rapid business growth and operational complexity Hands-on experience with Big Data, data platforms, and data pipelines Experience managing in mid-stage or growth-stage startups, particularly in logistics-heavy, scheduling-driven, or operationally complex environments Understanding of data privacy, HIPAA compliance, and security best practices in healthcare or other regulated industries Interview Process: We aim to complete the interview process between 2-3 weeks. It will usually consist of: Recruiter Screen (30-Minutes) Hiring Manager Introduction (30-Minutes) Technical Assessment (45-Minutes) Onsite Interview: Systems Design + Behavioral Interview + Lunch with the Team (3-hours) References What we offer: Meaningful pre-IPO equity Medical, dental, and vision plans 100% paid for you and your dependents Flexible PTO + 10 paid holidays per year 401(k) with match 16-week parental leave policy for birthing parent, 8 weeks for all other parents HSA + FSA contributions Life insurance, plus short and long-term disability coverage Free daily lunch in-office Annual learning stipend Apply for this Job Powered by Privacy PolicySecurityVulnerability Disclosure

Listed by Sprinter Health for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

64 days ago

Product Engineering Team - Software Engineer (Mid-Level)

Sprinter Health · San Francisco, California, United States

Pay

$165k–200k

Setting

Hybrid

Apply Sign in to favorite Permalink

Product Engineering Team - Software Engineer (Mid-Level) Location San Francisco, CA Address 394 Pacific Avenue , San Francisco, California, 94111 Employment Type Full time Location Type Hybrid Department Sprinter Health Engineering Compensation $165K – $200K • Offers Equity Overview Application About Sprinter Health: At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system—driving over $300B in avoidable costs every year. By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway. About the Role We’re looking for a Backend or Full-Stack Software Engineer (2-3 years experience) who wants to build the core systems that power last-mile healthcare delivery. At Sprinter, you’ll work on products that blend logistics, patient experience, safety, and medical operations—building and scaling backend services, data flows, integrations, and in-product experiences that directly determine whether patients get care. From routing clinicians and powering booking flows to integrating medical devices and preventing clinical errors, you’ll own complex, high-impact systems from 0→1. If you want to solve real problems with code and ship product that changes access to care, this is that role. Hybrid & Office Experience We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days. We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most. Lunch is provided every day, and the entire team takes an hour to eat together. It's one of the ways we stay connected outside of meetings. You'll usually find us playing a board game before getting back to work. What you will you do: Design and build backend services and APIs that power patient booking, clinician routing, logistics, and device integrations Ship full-stack product features end to end, from data modeling and business logic to UI and user flows (if full-stack) Solve complex operational challenges like scheduling, dispatch, safety checks, and error prevention at scale Integrate with external health systems, telemedicine platforms, and medical devices in a reliable, secure way Work closely with product, data, ops, and clinical teams to turn real-world problems into shipped software Own projects from 0→1, make architectural decisions, and help evolve our engineering practices as we scale What you have done: Spent 2+ years building and scaling backend or full-stack systems in production Designed APIs, data models, and services that power real user-facing products Led projects or pods end to end — from architecture and planning to delivery and iteration Mentored junior and mid-level engineers through code reviews, pairing, and technical guidance Shipped features independently and in collaboration with product, data, ops, or design partners Built in environments where speed, reliability, and ambiguity coexist — startups, high-growth teams, or 0→1 products Made technical decisions that balanced execution speed, scalability, and long-term maintainability What gives you an edge: You’ve built or scaled products in health tech, logistics, or other operationally complex environments You understand supply/demand dynamics — forecasting, routing, scheduling, or inventory management You’ve scaled backend systems to support rapid growth, high-volume data, and evolving business needs You’ve worked in mid- or growth-stage startups (Series A–C) where speed and ambiguity were the norm You’re comfortable working with data platforms, pipelines, or large-scale data processing You know your way around regulated environments — HIPAA, security, and privacy best practices The Interview Process We aim to complete the interview process between 2-3 weeks. It will usually consist of: Recruiter Screen (30-Minutes) Technical Assessment (45-Minutes) Hiring Manager Introduction (30-Minutes) Onsite Interview: Systems Design + Behavioral Interview + Lunch with the Team (3-hours) References What we offer Meaningful pre-IPO equity Medical, dental, and vision plans 100% paid for you and your dependents Flexible PTO + 10 paid holidays per year 401(k) with match 16-week parental leave policy for birthing parent, 8 weeks for all other parents HSA + FSA contributions Life insurance, plus short and long-term disability coverage Free daily lunch in-office Annual learning stipend Our Technology Stack Serverless AWS (AppSync, DynamoDB, Lambda, Amplify, CloudFormation, Node) React Native, React Native for Web GraphQL Typescript Javascript Node.js Sprinter Health is an equal opportunity employer. We value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or other protected classes. Beware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers. If you are applying for a job, you can confirm the legitimacy of a job posting by viewing current open roles here. All legitimate job postings will require an application to be made directly on our official Sprinter Health Careers website. Job-related communications will only be sent from email addresses ending in @sprinterhealth.com. Please ensure that you’re only replying to emails that end with @sprinterhealth.com. Apply for this Job Powered by Privacy PolicySecurityVulnerability Disclosure

Listed by Sprinter Health for a position based in the United States. Employers on this board attest they are hiring domestically.

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