Machine Learning Engineer (Staff)

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 .

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