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Staff AI Platform Engineer
Afresh · San Francisco, CA
Pay
$169k–273k
Setting
On-site
Back to jobs
Staff AI Platform Engineer
San Francisco, CA
Apply
Afresh, the AI platform for grocery, began by tackling the most complex problem in the industry: fresh, and has evolved into the core AI platform for grocers.
By leveraging proprietary AI designed for high-volatility environments, we empower partners like Albertsons, Meijer, and Wakefern to drive smarter decisions across their entire enterprise.
Following record-breaking 70% revenue growth in 2025, we have scaled to 6 enterprise-grade solutions, with solutions live in over 10% of the U.S. grocery market. Our platform now orchestrates billions of decisions from the store floor to the distribution center and prevented over 200 million pounds of food waste last year alone.
If you're looking for a role where your work directly translates into massive scale and social good, and you want to be part of the team that defines how the world eats, there is no better time to join us.
About the Role
Frontier models are a commodity. The knowledge you feed them is not.
As a Staff AI Platform Engineer, you build the AI and data platform that powers Afresh's products: the knowledge and retrieval layer that makes grocery data reliably usable by LLMs, the agent systems built on top of it, and the evaluation and serving infrastructure underneath. Your "customers" are Afresh's own engineers and AI products — your job is to give them a platform that turns raw grocery data into context a model can be trusted with, at production quality.
This is senior, 0-to-1 platform work. You'll make foundational choices about how we represent grocery knowledge, ground our models, and measure whether any of it is actually working — in a fast-moving space with no playbook.
What You’ll Do
Build the knowledge & retrieval layer
Design and operate the knowledge graph and ontology that capture how grocery data relates.
Build the retrieval systems (vector, graph, and structured) that feed the right context to our models — so grounding is reliable, not lucky.
Build and serve the agent platform
Build LLM-powered agents (tool-use, multi-step reasoning, orchestration) and the serving infrastructure to run them reliably and cost-effectively.
Build the tools, abstractions, and interfaces other engineers depend on — a platform, not one-off features.
Own evaluation, quality, and the data foundation
Stand up eval sets, LLM-as-judge harnesses, tracing, and observability, plus the metrics (faithfulness, accuracy, hallucination rate, latency, cost) that tell us whether a change helped or hurt.
Build the pipelines, data products, and experimentation on Databricks/MLflow that take work from prototype to production — and partner with the engineers deploying our AI in the field to harden what works into reusable capabilities.
What Makes You a Good Fit
We encourage all highly-qualified candidates to apply, even if they do not fulfill all the listed criteria.
5+ years building production software, data, or ML systems; an excellent engineer with strong systems and API design (Python).
Hands-on production experience with LLM systems: retrieval/RAG, agents and tool-use, prompt and context engineering — and, critically, evaluation. You measure quality; you don't eyeball it.
Solid data-engineering and data-platform foundations: pipelines, data modeling, and a modern cloud data stack (Databricks/Spark, MLflow, cloud warehouses).
Comfort in the messy middle of AI systems — retrieval quality, latency and cost trade-offs, non-determinism — and the instinct to build the guardrails and evals that make them trustworthy.
A platform mindset: you build for leverage and clean interfaces, and you thrive in ambiguity in a fast-moving space.
Nice to Have
Knowledge graphs, ontologies, or semantic layers in production; graph databases.
Vector stores (pgvector, Pinecone, Weaviate, etc.) and hybrid search.
MCP or similar tool/context protocols; agent frameworks (e.g., LangGraph).
MLOps and model serving at scale; experimentation and observability tooling for LLM systems.
Experience in grocery, retail, or other complex enterprise data domains.
Our Tech Stack
Python, PySpark, dbt
Databricks (Delta Lake, Unity Catalog, MLflow)
Astronomer (Airflow) for orchestration
LLMs and agents (Claude), retrieval over vector + graph stores, MCP tooling, eval and observability harnesses
Claude, GitHub, Shortcut, Notion for development workflows
Why Afresh?
We're a mission-driven company that eliminates hundreds of millions of pounds of food waste in grocery stores every year — your platform powers products with direct, visible impact.
Build the foundation the rest of the product stands on: high-leverage, 0-to-1 work where your abstractions make the whole team faster.
Be part of an engineering culture that's genuinely AI-forward — we want to be on the bleeding edge of agentic systems, not watching from the sidelines.
Senior team, high trust, and real ownership at a pivotal inflection point for how Afresh scales.
Collaborative, supportive environment & awesome people :)
This is a hybrid role based in the San Francisco office (3 days/week)
This position is not eligible for company sponsorship.
Salary Band in U.S. (USD): $168,912-$253,368 + meaningful early-stage equity + benefits
Why You’ll Love Working at Afresh
At Afresh, our mission to eliminate food waste starts with investing in our people. We provide a comprehensive support system designed to help you do your best work while maintaining a healthy, balanced life.
Comprehensive Health & Wellness: Comprehensive medical, dental, and vision coverage for you and your family, with the majority of premiums covered by Afresh. We also provide dedicated mental health support and counseling services.
Invested in Your Future: Competitive base salary, meaningful equity (U.S. employees), and a 401(k) program with a generous company match.
Flexible & Modern Workspace: Whether you work from home or a local office, we support your setup with a home office stipend and "Coworking Wallets" for flexible workspace access.
Growth-Obsessed Culture: We believe in continuous learning. Every employee receives an annual professional development budget to master new skills and grow their career at Afresh.
Holistic Monthly Stipends: Beyond your paycheck, we provide monthly stipends for "Betterment" (wellness/lifestyle) and telecommunications to ensure you have what you need to thrive.
Time to Recharge: Flexible paid time off to take the time you need to recharge.
*Full-time U.S. employees are eligible for these benefits
About Afresh
Founded in 2017, Afresh is using AI to tackle the #1 solution to curb climate change: reducing food waste. By building AI specifically for the intricacies of grocery—from the fresh perimeter to the center store—we help grocers minimize waste and maximize sales.
Afresh sits at an incredible intersection of positive social impact, rocket ship financial growth, and cutting-edge technology. Our best-in-class AI research has been published in top journals, including ICML, and our investors include Al Gore’s Just Climate, former Whole Foods Market CEO Walter Robb, and Eric Schmidt's Innovation Endeavors.
Grocery is the past, present, and future of our food system – the waste we create today will impact our planet for years to come. Join us as we continue to build a vibrant, diverse, and inclusive team that embodies our company’s values of proactivity, kindness, candor, and humility.
Afresh provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity/expression, marital status, pregnancy or related condition, or any other basis protected by law.
Here at Afresh, many of our employees work remotely provided that they reside in one of the following states: AL, AR, CA, CO, FL, GA, IL, KY, MA, MI, MT, MO, NV, NJ, NY, NC, OR, PA, TX, WA, UT, VA, WI.
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This role is hybrid and based in San Francisco, CA, with 3 days per week onsite in our headquarters. Are you currently based in or able to relocate to the San Francisco Bay Area, and able to work on-site in a hybrid capacity?*
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Do you have 3+ years of professional experience building production software, data, or ML systems, with strong systems and API design skills in Python?*
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Listed by Afresh for a position based in the United States. Employers on this board attest they are hiring domestically.
Senior AI Platform Engineer
Afresh · San Francisco, CA
Pay
$156k–231k
Setting
On-site
Back to jobs
Senior AI Platform Engineer
San Francisco, CA
Apply
Afresh, the AI platform for grocery, began by tackling the most complex problem in the industry: fresh, and has evolved into the core AI platform for grocers.
By leveraging proprietary AI designed for high-volatility environments, we empower partners like Albertsons, Meijer, and Wakefern to drive smarter decisions across their entire enterprise.
Following record-breaking 70% revenue growth in 2025, we have scaled to 6 enterprise-grade solutions, with solutions live in over 10% of the U.S. grocery market. Our platform now orchestrates billions of decisions from the store floor to the distribution center and prevented over 200 million pounds of food waste last year alone.
If you're looking for a role where your work directly translates into massive scale and social good, and you want to be part of the team that defines how the world eats, there is no better time to join us.
About the Role
Frontier models are a commodity. The knowledge you feed them is not.
As a Senior AI Platform Engineer, you build the AI and data platform that powers Afresh's products: the knowledge and retrieval layer that makes grocery data reliably usable by LLMs, the agent systems built on top of it, and the evaluation and serving infrastructure underneath. Your "customers" are Afresh's own engineers and AI products — your job is to give them a platform that turns raw grocery data into context a model can be trusted with, at production quality.
This is senior, 0-to-1 platform work. You'll make foundational choices about how we represent grocery knowledge, ground our models, and measure whether any of it is actually working — in a fast-moving space with no playbook.
What You’ll Do
Build the knowledge & retrieval layer
Design and operate the knowledge graph and ontology that capture how grocery data relates.
Build the retrieval systems (vector, graph, and structured) that feed the right context to our models — so grounding is reliable, not lucky.
Build and serve the agent platform
Build LLM-powered agents (tool-use, multi-step reasoning, orchestration) and the serving infrastructure to run them reliably and cost-effectively.
Build the tools, abstractions, and interfaces other engineers depend on — a platform, not one-off features.
Own evaluation, quality, and the data foundation
Stand up eval sets, LLM-as-judge harnesses, tracing, and observability, plus the metrics (faithfulness, accuracy, hallucination rate, latency, cost) that tell us whether a change helped or hurt.
Build the pipelines, data products, and experimentation on Databricks/MLflow that take work from prototype to production — and partner with the engineers deploying our AI in the field to harden what works into reusable capabilities.
What Makes You a Good Fit
We encourage all highly-qualified candidates to apply, even if they do not fulfill all the listed criteria.
3+ years building production software, data, or ML systems; an excellent engineer with strong systems and API design (Python).
Hands-on production experience with LLM systems: retrieval/RAG, agents and tool-use, prompt and context engineering — and, critically, evaluation. You measure quality; you don't eyeball it.
Solid data-engineering and data-platform foundations: pipelines, data modeling, and a modern cloud data stack (Databricks/Spark, MLflow, cloud warehouses).
Comfort in the messy middle of AI systems — retrieval quality, latency and cost trade-offs, non-determinism — and the instinct to build the guardrails and evals that make them trustworthy.
A platform mindset: you build for leverage and clean interfaces, and you thrive in ambiguity in a fast-moving space.
Nice to Have
Knowledge graphs, ontologies, or semantic layers in production; graph databases.
Vector stores (pgvector, Pinecone, Weaviate, etc.) and hybrid search.
MCP or similar tool/context protocols; agent frameworks (e.g., LangGraph).
MLOps and model serving at scale; experimentation and observability tooling for LLM systems.
Experience in grocery, retail, or other complex enterprise data domains.
Our Tech Stack
Python, PySpark, dbt
Databricks (Delta Lake, Unity Catalog, MLflow)
Astronomer (Airflow) for orchestration
LLMs and agents (Claude), retrieval over vector + graph stores, MCP tooling, eval and observability harnesses
Claude, GitHub, Shortcut, Notion for development workflows
Why Afresh
We're a mission-driven company that eliminates hundreds of millions of pounds of food waste in grocery stores every year — your platform powers products with direct, visible impact.
Build the foundation the rest of the product stands on: high-leverage, 0-to-1 work where your abstractions make the whole team faster.
Be part of an engineering culture that's genuinely AI-forward — we want to be on the bleeding edge of agentic systems, not watching from the sidelines.
Senior team, high trust, and real ownership at a pivotal inflection point for how Afresh scales.
Collaborative, supportive environment & awesome people :)
This is a hybrid role based in the San Francisco office (3 days/week)
This position is not eligible for company sponsorship.
Salary Range in U.S.: $156,060 - $211,140 + meaningful early-stage equity + benefits
Why You’ll Love Working at Afresh
At Afresh, our mission to eliminate food waste starts with investing in our people. We provide a comprehensive support system designed to help you do your best work while maintaining a healthy, balanced life.
Comprehensive Health & Wellness: Comprehensive medical, dental, and vision coverage for you and your family, with the majority of premiums covered by Afresh. We also provide dedicated mental health support and counseling services.
Invested in Your Future: Competitive base salary, meaningful equity (U.S. employees), and a 401(k) program with a generous company match.
Flexible & Modern Workspace: Whether you work from home or a local office, we support your setup with a home office stipend and "Coworking Wallets" for flexible workspace access.
Growth-Obsessed Culture: We believe in continuous learning. Every employee receives an annual professional development budget to master new skills and grow their career at Afresh.
Holistic Monthly Stipends: Beyond your paycheck, we provide monthly stipends for "Betterment" (wellness/lifestyle) and telecommunications to ensure you have what you need to thrive.
Time to Recharge: Flexible paid time off to take the time you need to recharge.
*Full-time U.S. employees are eligible for these benefits
About Afresh
Founded in 2017, Afresh is using AI to tackle the #1 solution to curb climate change: reducing food waste. By building AI specifically for the intricacies of grocery—from the fresh perimeter to the center store—we help grocers minimize waste and maximize sales.
Afresh sits at an incredible intersection of positive social impact, rocket ship financial growth, and cutting-edge technology. Our best-in-class AI research has been published in top journals, including ICML, and our investors include Al Gore’s Just Climate, former Whole Foods Market CEO Walter Robb, and Eric Schmidt's Innovation Endeavors.
Grocery is the past, present, and future of our food system – the waste we create today will impact our planet for years to come. Join us as we continue to build a vibrant, diverse, and inclusive team that embodies our company’s values of proactivity, kindness, candor, and humility.
Afresh provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity/expression, marital status, pregnancy or related condition, or any other basis protected by law.
Here at Afresh, many of our employees work remotely provided that they reside in one of the following states: AL, AR, CA, CO, FL, GA, IL, KY, MA, MI, MT, MO, NV, NJ, NY, NC, OR, PA, TX, WA, UT, VA, WI.
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Do you have unrestricted work authorization in the United States? *
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Will you now, or in the future, require immigration sponsorship for continued employment in the United States?*
Select...
This role is hybrid and based in San Francisco, CA, with 3 days per week onsite in our headquarters. Are you currently based in or able to relocate to the San Francisco Bay Area, and able to work on-site in a hybrid capacity?*
Select...
Do you have 3+ years of professional experience building production software, data, or ML systems, with strong systems and API design skills in Python?*
Select...
Have you personally designed, built, and shipped a production AI/ML or LLM-based system at your current or a previous company (not a prototype, hackathon project, or demo)?*
Select...
Briefly describe the production AI system you built: what it does, your role, and what "production" meant in that context (e.g., scale, uptime, real users/data). *
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Listed by Afresh for a position based in the United States. Employers on this board attest they are hiring domestically.
Senior Forward Deployed Engineer
Afresh · San Francisco, CA
Pay
$156k–231k
Setting
On-site
Back to jobs
Senior Forward Deployed Engineer
San Francisco, CA
Apply
Afresh, the AI platform for grocery, began by tackling the most complex problem in the industry: fresh, and has evolved into the core AI platform for grocers.
By leveraging proprietary AI designed for high-volatility environments, we empower partners like Albertsons, Meijer, and Wakefern to drive smarter decisions across their entire enterprise.
Following record-breaking 70% revenue growth in 2025, we have scaled to 6 enterprise-grade solutions, with solutions live in over 10% of the U.S. grocery market. Our platform now orchestrates billions of decisions from the store floor to the distribution center and prevented over 200 million pounds of food waste last year alone.
If you're looking for a role where your work directly translates into massive scale and social good, and you want to be part of the team that defines how the world eats, there is no better time to join us.
About the Role
Most companies make you choose: build the platform, or go deploy it. Here you do both — and that's the point.
As a Forward Deployed AI Engineer, you're part of a single team that both delivers Afresh's AI into enterprise grocery customers and builds the platform that makes that delivery fast. You'll spend dedicated time in the field — embedded with a customer, integrating into their data, shipping AI systems on top of it — and dedicated time on the platform, turning what you just learned into reusable tooling the whole team deploys next. You build the house you live in.
Afresh leads the customer relationship and direction; you and a small team bring the technical firepower — scope and architect the work with the customer, then build it. Because you also own the platform underneath, the rough edges you hit in the field become the things you fix at the root.
This is senior, hands-on, 0-to-1 work in a space with no playbook.
What You'll Do
In the field (forward deployed)
Partner with Afresh's account lead and the customer's technical teams to scope and architect the work — the data sources, the architecture, and the path to production.
Embed with the customer's data and engineering teams (remote and on-site); integrate into their cloud and data platform; build production-grade pipelines and model messy enterprise data into trustworthy data products.
Design and ship LLM- and agent-powered systems on that data — retrieval, agentic workflows, data-quality and analytics agents — reliable enough to run in production, not just to demo.
On the platform (building the house you live in)
Harden what works in the field into the shared platform: the knowledge and grounding layer (knowledge graph, ontology, and retrieval) that makes grocery data usable by LLMs, the agent frameworks, and the serving infrastructure.
Build the evals, tracing, and tooling that let the team measure quality — accuracy, hallucination rate, latency, cost — and ship faster on the next customer.
Build for leverage: clean interfaces and reusable building blocks, not one-off per-customer code.
Across both
Own the flywheel: field learnings flow straight into the platform, and platform improvements show up at the next customer.
What Makes You a Great Fit
We encourage all highly-qualified candidates to apply, even if they do not fulfill all the listed criteria.
3+ years building production software and data systems, with strong, production-grade code
An architect's instinct: you can take an ambiguous problem and a messy data landscape, design a clean and workable solution, and then build it.
Genuine AI/LLM depth — you've built real systems with LLMs and agents (retrieval/RAG, tool-use) and you evaluate quality rather than eyeball it.
Real data-engineering depth: building and operating data pipelines, modeling messy enterprise data, and working in a modern cloud data platform (Databricks, BigQuery, Snowflake, or similar).
Range across both modes — you genuinely like being in front of customers and going heads-down to build reusable infrastructure, and you can switch between them without one suffering. This is the role's defining trait.
Customer-facing comfort: you work well with a customer's engineers and data teams — running working sessions, explaining your thinking, and earning trust through what you deliver.
A bias toward ownership and momentum, and comfort traveling to customer sites regularly (~10-20%).
Nice to Have
Experience in grocery, retail, or supply chain data domains.
Knowledge graphs, ontologies, or semantic layers in production; graph and vector stores (pgvector, Pinecone, Weaviate) and hybrid search.
MCP or similar tool/context protocols; agent frameworks (e.g., LangGraph); MLOps, model serving, and observability for LLM systems.
Prior forward-deployed, solutions, or implementation engineering — or early-stage startup experience navigating rapid customer expansion.
Why Afresh?
We're a mission-driven company that eliminates hundreds of millions of pounds of food waste in grocery stores every year — your work has direct, visible impact.
You'll do both halves of the job: deploy with customers and build the platform you deploy — never a body-shop consultant, never an ivory-tower platform engineer.
Be part of an engineering culture that's genuinely AI-forward — we want to be on the bleeding edge of agentic development, not watching from the sidelines.
Senior team, high trust, and real ownership at a pivotal inflection point for how Afresh scales.
Collaborative, supportive environment & awesome people
This is a hybrid role based in the San Francisco office (3 days/week)
This position is not eligible for company sponsorship.
Salary Range in U.S.: $156,060 - $211,140 + meaningful early-stage equity + benefits
Why You’ll Love Working at Afresh
At Afresh, our mission to eliminate food waste starts with investing in our people. We provide a comprehensive support system designed to help you do your best work while maintaining a healthy, balanced life.
Comprehensive Health & Wellness: Comprehensive medical, dental, and vision coverage for you and your family, with the majority of premiums covered by Afresh. We also provide dedicated mental health support and counseling services.
Invested in Your Future: Competitive base salary, meaningful equity (U.S. employees), and a 401(k) program with a generous company match.
Flexible & Modern Workspace: Whether you work from home or a local office, we support your setup with a home office stipend and "Coworking Wallets" for flexible workspace access.
Growth-Obsessed Culture: We believe in continuous learning. Every employee receives an annual professional development budget to master new skills and grow their career at Afresh.
Holistic Monthly Stipends: Beyond your paycheck, we provide monthly stipends for "Betterment" (wellness/lifestyle) and telecommunications to ensure you have what you need to thrive.
Time to Recharge: Flexible paid time off to take the time you need to recharge.
*Full-time U.S. employees are eligible for these benefits
About Afresh
Founded in 2017, Afresh is using AI to tackle the #1 solution to curb climate change: reducing food waste. By building AI specifically for the intricacies of grocery—from the fresh perimeter to the center store—we help grocers minimize waste and maximize sales.
Afresh sits at an incredible intersection of positive social impact, rocket ship financial growth, and cutting-edge technology. Our best-in-class AI research has been published in top journals, including ICML, and our investors include Al Gore’s Just Climate, former Whole Foods Market CEO Walter Robb, and Eric Schmidt's Innovation Endeavors.
Grocery is the past, present, and future of our food system – the waste we create today will impact our planet for years to come. Join us as we continue to build a vibrant, diverse, and inclusive team that embodies our company’s values of proactivity, kindness, candor, and humility.
Afresh provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity/expression, marital status, pregnancy or related condition, or any other basis protected by law.
Here at Afresh, many of our employees work remotely provided that they reside in one of the following states: AL, AR, CA, CO, FL, GA, IL, KY, MA, MI, MT, MO, NV, NJ, NY, NC, OR, PA, TX, WA, UT, VA, WI.
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Listed by Afresh for a position based in the United States. Employers on this board attest they are hiring domestically.
Cyber Security Intern
Labelbox · San Francisco Bay Area
Pay
$40k–85k
Setting
On-site
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Cyber Security Intern
San Francisco Bay Area
Apply
Shape the Future of AI
At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.
About Labelbox
We're the only company offering three integrated solutions for frontier AI development:
Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale
Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models
Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling
Why Join Us
High-Impact Environment: We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions.
Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.
Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.
Continuous Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.
Clear Ownership: You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.
Role Overview
As a Security Engineering Intern at Labelbox, you will work closely with our security team to strengthen and scale our enterprise security program. You will receive direct mentorship while gaining hands-on experience across cloud security, application security, SecOps, and compliance workflows. Your work will help improve the security posture of our infrastructure, services, and internal tooling. This is an hourly internship position.
Your Impact
Support day to day triage and analysis of incoming security issues, including operating our Vulnerability Management program.
Contribute to improving access workflows in our identity and access management systems.
Audit and refine access controls for core data platforms and cloud resources.
Assist in strengthening CI/CD security by integrating automated checks that enforce secure development practices early in the lifecycle.
Help operate and optimize our Security Information and Event Management (SIEM) platform by tuning alerts, improving log ingestion, and building lightweight automations.
Participate in major internal security initiatives, such as supporting a Cybersecurity Tabletop Exercise and documenting findings.
Research and evaluate enhancements to our security tool stack and contribute to related internal documentation and optimization efforts.
What You Bring
We are looking for a highly motivated and curious individual with a strong interest in the challenges of modern security engineering.
Currently pursuing a Bachelor's or Master's degree in Cybersecurity, Computer Science, Information Technology, or a related field.
A demonstrated interest in cloud security, data security, and security operations.
High degree of ownership, initiative, and willingness to dive into complex technical problems.
Basic understanding of cloud platforms such as GCP, AWS, or Azure, as well as foundational database concepts including SQL or data warehousing.
Demonstrated ability to integrate modern AI tools and local LLM agents (such as Cursor or Claude Code) into your workflow to increase efficiency and solve technical problems faster.
Strong analytical, organizational, and time management skills.
Excellent written and verbal communication abilities, including clear documentation of technical findings.
Curiosity, eagerness to learn, and a desire to grow through mentorship across diverse areas of security engineering.
Security Engineering at Labelbox
Our Security Engineering team partners closely with engineering and infrastructure teams to ensure we deliver secure, reliable, and trusted systems for our customers. We focus on building scalable security controls, driving secure development practices, and continually evolving our detection, response, and risk management capabilities. Interns on our team gain exposure to real world security challenges while contributing directly to initiatives that support Labelbox’s growth and security maturity.
See our recent Blog about security at Labelbox: https://labelbox.com/blog/engineering-trust-in-an-autonomous-world/
Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.
Annual base salary range
$40 - $55 USD
Life at Labelbox
Location: Join our dedicated tech hub in San Francisco
Work Style: Hybrid model with 3 days per week in office, combining collaboration and flexibility
Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making
Growth: Career advancement opportunities directly tied to your impact
Vision: Be part of building the foundation for humanity's most transformative technology
Our Vision
We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs.
Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs.
Your Personal Data Privacy: Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice.
Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.
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Listed by Labelbox for a position based in the United States. Employers on this board attest they are hiring domestically.
Principal Security Field Engineer
Databricks · Remote - California
Pay
$216k–372k
Setting
Remote
Principal Security Field Engineer, Remote - California. Join us! Together we can use data to solve the challenges of tomorrow
Listed by Databricks for a position based in the United States. Employers on this board attest they are hiring domestically.
Principal Security Field Engineer
Databricks · Remote - California
Pay
$216k–372k
Setting
Remote
Principal Security Field Engineer, Remote - California. Join us! Together we can use data to solve the challenges of tomorrow
Listed by Databricks for a position based in the United States. Employers on this board attest they are hiring domestically.
Senior Solutions Consultant
Outreach.io · United States
Pay
$135k–170k
Setting
Remote
About Outreach
Outreach, founded in 2014, is the only complete platform for revenue teams. Outreach infuses agentic AI, conversation intelligence, and assistive AI to power hundreds of use cases across revenue motions. From new logo prospecting to expansions, deal acceleration, driving retention, and forecasting, Outreach AI automates workflows and frees sellers to focus on more strategic conversations and actions. Revenue leaders benefit from connected account visibility, performance insights, and higher forecasting accuracy across every GTM team. World leading enterprise organizations use Outreach to power their revenue teams, including Databricks, SAP, Siemens, and Verizon to name a few.
About the Team
This role will report into our Solutions Consulting team. The Solutions Consulting team is an integral part of the Outreach Go-To-Market function. We focus on driving real business impact for customers by being both technology and domain experts. We are expected to understand our customer’s challenges and objectives - then guide them toward lasting solutions. We do this by partnering with other functions, internally (professional services, product, engineering, success, marketing) and externally (partners).
The Role
Working in a fast-growth company means that no day is ever the same! Here are the main responsibilities and tasks you’ll undertake while working with us:
Listed by Outreach.io for a position based in the United States. Employers on this board attest they are hiring domestically.
Staff Software Engineer - Data Infrastructure
Plaid · San Francisco, California, United States
Pay
$208k–274k
Setting
Hybrid
Staff Software Engineer - Data Infrastructure
Location
San Francisco HQ
Address
1098 Harrison Street, San Francisco, California, 94103
Employment Type
Full time
Location Type
Hybrid
Department
All Departments
Engineering
Compensation
Zone 1
$207.6K – $273.6K • Offers Equity
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
Overview
Application
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
Making data driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide tooling and guidance to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively.
Engineers on Data Infrastructure are domain experts in Data Warehouse, Data Lakehouse, Spark, Workflow Orchestration, and Streaming technologies. We scale our existing data pipelines in a performant and cost efficient way while creating the necessary abstractions to make developing on top of this platform extremely simple for other engineers at Plaid.
Responsibilities
Contribute towards the long-term technical roadmap for data-driven and machine learning iteration at Plaid
Leading key data infrastructure projects such as improving ML development golden paths, implementing offline streaming solutions for data freshness, building net new ETL pipeline infrastructure, and evolving data warehouse or data lakehouse capabilities.
Working with stakeholders in other teams and functions to define technical roadmaps for key backend systems and abstractions across Plaid.
Debugging, troubleshooting, and reducing operational burden for our Data Platform.
Growing the team via mentorship and leadership, reviewing technical documents and code changes.
Qualifications
6+ years of software engineering experience
Extensive hands-on software engineering experience, with a strong track record of delivering successful projects within the Data Infrastructure or Platform domain at similar or larger companies.
Deep understanding of one of the below:
Data Infrastructure systems, including Data Warehouses, Data Lakehouses, Apache Spark, Streaming Infrastructure, Workflow Orchestration.
Strong cross-functional collaboration, communication, and project management skills, with proven ability to coordinate effectively.
Proficiency in coding, testing, and system design, ensuring reliable and scalable solutions.
Demonstrated leadership abilities, including experience mentoring and guiding junior engineers.
Nice-to-Have:
Experience with Databricks
Experience with Airflow
Experience with AWS EMR
Experience with Python
Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com.
Please review our Candidate Privacy Notice here.
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
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Listed by Plaid for a position based in the United States. Employers on this board attest they are hiring domestically.
Sr. Engineering Manager, AI Runtime
Databricks · Mountain View, California
Pay
$229k–297k
Setting
On-site
Sr. Engineering Manager, AI Runtime, Mountain View, California; San Francisco, California. Join us! Together we can use data to solve the challenges of tomorrow
Listed by Databricks for a position based in the United States. Employers on this board attest they are hiring domestically.
Staff Software Engineer - Data Infrastructure
Plaid · San Francisco, California, United States
Pay
$208k–274k
Setting
Hybrid
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
Making data driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide tooling and guidance to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively.
Engineers on Data Infrastructure are domain experts in Data Warehouse, Data Lakehouse, Spark, Workflow Orchestration, and Streaming technologies. We scale our existing data pipelines in a performant and cost efficient way while creating the necessary abstractions to make developing on top of this platform extremely simple for other engineers at Plaid.
Responsibilities
- Contribute towards the long-term technical roadmap for data-driven and machine learning iteration at Plaid
- Leading key data infrastructure projects such as improving ML development golden paths, implementing offline streaming solutions for data freshness, building net new ETL pipeline infrastructure, and evolving data warehouse or data lakehouse capabilities.
- Working with stakeholders in other teams and functions to define technical roadmaps for key backend systems and abstractions across Plaid.
- Debugging, troubleshooting, and reducing operational burden for our Data Platform.
- Growing the team via mentorship and leadership, reviewing technical documents and code changes.
Qualifications
- 6+ years of software engineering experience
- Extensive hands-on software engineering experience, with a strong track record of delivering successful projects within the Data Infrastructure or Platform domain at similar or larger companies.
- Deep understanding of one of the below:
- Data Infrastructure systems, including Data Warehouses, Data Lakehouses, Apache Spark, Streaming Infrastructure, Workflow Orchestration.
- Strong cross-functional collaboration, communication, and project management skills, with proven ability to coordinate effectively.
- Proficiency in coding, testing, and system design, ensuring reliable and scalable solutions.
- Demonstrated leadership abilities, including experience mentoring and guiding junior engineers.
- Nice-to-Have:
- Experience with Databricks
- Experience with Airflow
- Experience with AWS EMR
- Experience with Python
Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com.
Please review our Candidate Privacy Notice here https://plaid.com/legal/#candidate-privacy-notice.
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
Listed by Plaid for a position based in the United States. Employers on this board attest they are hiring domestically.
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