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At Sprinter Health, our mission is to dramatically expand access to healthcare by reimagining the patient experience—delivered at home and powered by technology for scale.

We're building the technology and clinical services platform to make preventive, connected healthcare accessible for everyone in the US. We deliver hybrid care (in-home + virtual) backed by data products across 15+ states, serving 60%+ of the US population through major health plans and systems. With 1M+ patients and 92+ NPS, we're rapidly scaling our impact. Our team of technologists, clinicians, and operators is backed by investors including a16z, General Catalyst, GV, and Accel that have funded companies like Devoted Health, Livongo, Benchling, Stripe, Ramp, Airbnb, Lyft, Instagram, and Databricks.

We have a rapidly growing team of visionary leaders who are passionate about increasing access to care, lowering healthcare costs, and improving outcomes for patients.

About The Role

We're rapidly growing our clinical team as we cultivate new partnerships with enterprises across the healthcare spectrum. We're looking for an experienced Registered Nurse (RN) to work as a Clinical Manager to oversee our west coast region as well as future markets in the nation as we continue to expand.

This role will report to the Director of In Home Care and you will collaborate closely with all Clinical Managers and internal stakeholders to effectively lead the charge of the Sprinters in your assigned regions. The ideal candidate will be an experienced leader with demonstrated ability to effectively manage teams, identify and create solutions.

Clinical Operations Management

Oversee the daily operations of the clinical field team, ensuring smooth workflow.
Develop and implement policies and procedures to maintain high standards of care.
Monitor patient care quality, ensuring adherence to clinical guidelines and best practices.

Staff Supervision & Development

Recruit, train, and manage the clinical Sprinter team.
Conduct performance evaluations and provide ongoing coaching and support.

Compliance & Quality Assurance

Ensure compliance with healthcare regulations (OSHA, HIPAA, Joint Commission, etc.).
Monitor incident reports and implement corrective actions as needed.
Lead quality improvement initiatives to enhance patient safety and service delivery.

Financial & Resource Management

Control expenses, and allocate resources effectively.
Optimize clinical workflows to improve efficiency and reduce costs.

Patient Care Coordination

Address patient concerns and ensure a patient-centered approach to care.
Respond to abnormal labs, tests, vitals- assess, triage patients to ensure proper follow up care is received.
Implement strategies to improve patient satisfaction and outcomes.

Collaboration & Communication

Work closely with the Clinical Manager team and cross functional teams to ensure Sprinters are supported in the field and all teams are aligned.
Attend management meetings and provide updates on clinical operations for your market.

You will thrive at Sprinter Health if you have:

The desire to work collaboratively with a tight-knit but quickly growing team across many different areas of the business
Flexibility, humility, and a sense of humor
Excitement for working in a fast-paced startup

Experience and Skills

5+ years of Registered Nurse experience
Experience in a healthcare startup or virtual management experience preferred
Direct report management experienceProficient at working with modern technology
Able to do extensive travel of up to 50%
This position will be a hybrid role where you will work some days from home and others travel to various coverage regions to meet

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.If you're looking for a career that offers opportunities for growth, continual development, professional challenge and the chance to make a real difference in the lives of people, apply today!

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

51 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

51 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

51 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.

Bio + Health

51 days ago

AI Enablement Engineer (Senior / Staff)

Sprinter Health · San Francisco, California, United States

Pay

$180k–260k

Setting

Hybrid

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AI Enablement Engineer (Senior / Staff) 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 AI Enablement Engineer to help every team at Sprinter build, adopt, and safely scale AI-powered workflows. This role is about turning AI from a set of tools into a company-wide operating advantage. You’ll work across engineering, operations, clinical, data, finance, and other teams to understand how work actually gets done, identify high-leverage opportunities for AI, and turn those opportunities into practical systems people can use. You’ll build bespoke agents, internal workflows, reusable templates, prompt and skill libraries, evaluation frameworks, deployment patterns, and training programs that raise AI fluency across the company. You’ll also help teams adopt AI coding assistants, agentic workflows, MCP servers, internal tools, and shared knowledge systems in ways that are useful, measurable, and safe around patient data. This is a hands-on builder role with a major enablement component. You should be as comfortable writing production-quality Python or TypeScript as you are running a workshop, facilitating office hours, or helping an operations lead understand how AI can improve a manual workflow. The ideal candidate is a builder, teacher, and systems thinker who measures success by what the whole organization can now do because of the tools, patterns, and examples you created. 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 Help define and drive Sprinter’s AI enablement strategy across engineering, operations, clinical, data, finance, and other functions Embed with teams to understand their workflows, identify high-leverage AI use cases, and translate business needs into working technical solutions Build bespoke agents, background workflows, internal tools, and automations that solve real operational, clinical, and engineering problems Create reusable playbooks, prompt libraries, skill libraries, workflow templates, and reference architectures that teams can self-serve Stand up shared context and knowledge systems that help AI tools ground answers in Sprinter’s data, documentation, codebases, and organizational context Evaluate, configure, and recommend AI tools, making practical build-versus-buy decisions based on team needs, safety, scalability, and cost Tune AI coding assistants and agentic workflows to Sprinter’s codebases, conventions, and development practices Build evaluation sets, benchmarks, and review patterns that help teams separate useful AI outputs from convincing-but-wrong ones Establish safe, repeatable deployment patterns for AI-built applications, internal tools, models, workflows, and data tables Partner with SRE, IT, Security, Legal, and clinical stakeholders on tool approval, deployment, access patterns, and PHI-safe guardrails Run recurring office hours, trainings, hackathons, and hands-on enablement sessions that build AI fluency across the company Measure AI adoption, productivity gains, quality improvements, and operational impact in ways that go beyond usage or token counts Communicate AI strategy, adoption progress, risks, and opportunities to individual contributors, managers, and executive leadership Help non-experts move quickly while ensuring patient safety, privacy, and quality are built into the workflow from the start What you have done Built production-quality software in Python, TypeScript, or similar languages Worked hands-on with LLMs, AI assistants, agents, tool calling, structured outputs, RAG, or other applied AI patterns Built internal tools, automations, workflows, developer productivity tooling, AI-enabled applications, or agentic systems Designed practical evaluations, benchmarks, or QA processes for AI workflows or software systems Worked with CI/CD, testing, deployment pipelines, or production release processes Gathered requirements from non-technical stakeholders and translated them into scoped, working technical solutions Enabled teams through documentation, training, office hours, workshops, hackathons, or reusable templates Used AI coding assistants such as Claude Code, Cursor, or similar tools as part of your day-to-day development workflow Made practical tradeoffs between speed, safety, usability, maintainability, and cost Communicated technical concepts clearly to audiences ranging from engineers to executives Operated in fast-moving, ambiguous environments where the path was not already defined What gives you an edge You have operated at Senior, Staff, or equivalent scope, driving technical decisions across multiple teams You’ve built internal AI platforms, agent frameworks, evaluation systems, workflow automation platforms, or developer productivity tooling You’ve helped a company or team adopt AI tools in a measurable, repeatable way You have experience standing up a centralized prompt library, skill library, workflow library, or knowledge/context hub You’ve worked with MCP servers, internal tool integrations, RAG systems, or AI agents connected to real business systems You have experience with healthcare data, PHI, HIPAA-aware workflows, or regulated environments You’ve partnered with security, IT, legal, compliance, or clinical teams to approve and deploy AI tools safely You have a public or internal track record of teaching, writing, workshops, talks, or training that made complex technical ideas accessible You’ve worked in a startup or high-growth environment where enablement, velocity, and practical judgment mattered What makes you successful You are a force multiplier and measure success by what the whole organization can now build with AI You meet teams where they are, ship the first working example, and turn it into a template others can reuse You reach for the simplest tool that safely solves the workflow You build for safety from the start through guardrails, evaluations, review patterns, and PHI-aware defaults You back adoption claims with evidence, including evals, benchmarks, productivity metrics, and quality improvements You teach as well as you build You can make AI make sense to an engineer, an operations lead, a clinician, and an executive You help people move faster without making patient safety or privacy someone else’s problem You create systems that make good AI usage easier and risky AI usage harder Day to Day In this role, you might spend your time: Pairing with an operations, clinical, engineering, or finance team to turn a manual workflow into a reliable AI-assisted process Building a self-testing agent or background workflow that solves a recurring internal problem Running AI office hours, facilitating a hackathon, or leading a hands-on training session Creating a reusable template, skill, prompt library, or workflow pattern for a common task Building an eval set with a team to test whether an AI workflow gives correct-first-time answers Setting up CI checks or deployment pipelines so AI-built applications and internal tools ship safely Evaluating a new AI tool and making a build-versus-buy recommendation Instrumenting adoption and reporting real productivity gains to leadership Writing guardrails, documentation, or review patterns that help non-experts move quickly and safely Partnering with IT, Security, SRE, or Legal to approve and deploy AI tools responsibly 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: AI enablement experience, technical depth, and cross-functional scope Hands-on Technical Assessment: Practical AI workflow building, software engineering, evaluation, 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.

Science

54 days ago

AI Research Scientist

Sprinter Health · San Francisco, California, United States

Pay

$160k–220k

Setting

Hybrid

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AI Research Scientist 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 $160K – $220K • 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 an AI Research Scientist to advance the methodological frontier of AI in healthcare. This role is ideal for someone who has demonstrated strong research taste, deep technical foundations, and the ability to turn open-ended problems into rigorous scientific contributions. You will develop and own a research agenda aligned with Sprinter’s company strategy. Your work may include novel architectures, new training or evaluation techniques, long-horizon research bets, peer-reviewed validation studies, patents, and methods that ultimately graduate into production systems. The ideal candidate is deeply technical, scientifically rigorous, and excited to collaborate closely with clinicians, product leaders, and applied AI teams. You understand that healthcare validation standards are higher than benchmark culture alone, and you are energized by the opportunity to produce research that is both scientifically meaningful and practically impactful. 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: Research Agenda & Scientific Contribution Develop and own a research agenda aligned with Sprinter’s long-term AI and company strategy. Identify open problems, position them against the literature, and design experiments that isolate meaningful contributions. Develop novel methods, architectures, training approaches, evaluation techniques, or validation frameworks. Produce publications, patents, peer-reviewed validation studies, and other evidence artifacts. Translate promising research into methods and tools that applied teams can use in production. Technical Leadership Raise the scientific bar across applied AI and engineering teams. Review methodologies, evaluation approaches, and experimental designs. Advise teams on hard technical decisions, especially around model performance, reliability, evaluation, uncertainty, and validation. Help determine whether a result is meaningful, reproducible, or an artifact. Mentor applied researchers and engineers on rigorous ML research practices. External Presence & Collaboration Maintain an external research presence through publications, talks, academic collaborations, and participation in relevant research communities. Collaborate with clinical partners on validation studies, including work that may involve IRB review, data governance, external validation, or prospective evaluation. Partner cross-functionally with Product, Clinical, Engineering, and Leadership teams to ensure research priorities map to meaningful company and patient impact. What you have done: Demonstrated ability to produce novel research, including identifying open problems, designing rigorous experiments, and writing work to a peer-review standard. Deep ML foundations and genuine depth in at least one relevant area, such as LLMs, agents, uncertainty, causality, multimodal learning, clinical AI, or related fields. Strong engineering ability, including the ability to run your own experiments at scale. Strong research taste and the ability to distinguish incremental work from meaningful methodological contribution. Comfort working in open-ended, ambiguous environments where the right research direction may need to be shaped from first principles. Interest in clinical collaboration and applied healthcare impact. Understanding of healthcare validation standards, including the importance of external validation, prospective evaluation, data governance, and real-world deployment constraints. What gives you an edge: First-author publications at top technical venues such as NeurIPS, ICML, ICLR, ACL, or related conferences. Publications in leading clinical AI or healthcare venues such as Nature Medicine, NEJM AI, npj Digital Medicine, CHIL, MLHC, or similar. Experience in academia, industry research labs, or research-heavy teams at AI-native healthcare companies. Experience collaborating with clinicians, clinical researchers, or healthcare operators. Familiarity with IRB processes, clinical data governance, or healthcare model validation. Dual literacy across machine learning and clinical collaboration. 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 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. 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.

Supply Chain

54 days ago

Applied Scientist, Optimization & Logistics

Sprinter Health · San Francisco, California, United States

Pay

$160k–220k

Setting

Hybrid

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Applied Scientist, Optimization & Logistics 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 – $220K • 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 an Applied Scientist to turn Sprinter’s hardest logistics problems into optimization models and decision systems that get the right clinician to the right patient at the right time. Sprinter runs a two-sided operation — clinicians on one side, patients who need care at home on the other — and we must match supply to demand across large regions under complex constraints. As an Applied Scientist, you will take ambiguous operational problems and shape them into well-posed tasks, strong baselines, and honest evaluations. The algorithms you build will answer questions like which clinician sees which patient, in what order, given drive time, appointment windows, and clinical constraints; how many clinicians to staff in each region next month; and how long a visit will take or whether a patient is likely to cancel. This role sits at the intersection of research and engineering, blending scientific rigor with a deployment-oriented mindset. It also requires close cross-functional partnership with operations, product, and engineering stakeholders. The ideal candidate is a scientist-engineer who reasons from first principles about uncertainty and constraints, reaches for the simplest model that works, and can move from a formulation on the whiteboard to a decision that runs in production. 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: Modeling & Optimization Turn ambiguous operational problems into well-posed optimization, forecasting, or simulation tasks. Build strong baselines and improve on them efficiently, adding complexity only when the value justifies it. Develop solutions across operations research, optimization, and machine learning, choosing the right tool for the problem. Run careful analysis and iterate toward decisions that improve real operational outcomes — cost per visit, clinician utilization, patient access, and visits completed. Evaluation & Scientific Rigor Design offline evaluations, simulated backtests, and live experiments that predict real-world operational impact. Find the gaps between a model’s assumptions and messy operational reality before they reach production. Choose metrics suited to stochastic, constrained, and partially observed operational systems. Interpret and communicate results effectively to cross-functional stakeholders. Collaboration & Delivery Partner with Engineering to productionize optimization and decision systems reliably. Work with operations partners and SMEs to validate assumptions and review where decisions break down. Explain tradeoffs, uncertainty, and limitations clearly to product and leadership. What you have done: Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation. Strong Python and SQL, the standard optimization and ML libraries, and the ability to run your own experiments end to end. Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow. Ability to turn an ambiguous problem into a well-posed optimization or forecasting task, discover and analyze related literature, and adapt/apply those methods to our tasks. Judgment about how uncertainty, constraints, and edge cases behave in real-world operational data. Interest in operations collaboration and applied healthcare impact. What gives you an edge: MS or PhD in operations research, industrial engineering, computer science, applied math, statistics, machine learning, or a related quantitative field; exceptional applied experience can substitute. Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting. Experience shipping optimization or decision systems that reached production and had material real-world impact. Hands-on experience with supply-and-demand matching in a marketplace, dispatch, or field-operations setting. Fluency deciding when an exact optimization approach beats a heuristic or learned one, and vice versa. 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.

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