{"id":16939,"url":"https://alion.io/job/sprinter-health-machine-learning-engineer-staff","title":"Machine Learning Engineer (Staff)","company":{"id":5046,"name":"Sprinter Health","domain":"sprinterhealth.com","url":"https://alion.io/company/sprinter-health","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":82,"open_postings":15,"ghost_share":0,"stale_share":0.933,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","Menlo Park, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":220000,"max":270000,"currency":"USD","period":"year","gross":null,"usd_annual":270000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Feature Store","optional":false},{"name":"HIPAA","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false}],"status":"live","first_seen_at":"2026-07-20T21:58:52Z","employer_posted_date":"2026-07-20","last_verified_at":"2026-10-17T22:49:12Z","board_verified":true,"closed_at":null,"days_open":89,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":89},"description":"Staff Machine Learning Engineer\nAbout Sprinter Health\nAt 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.\nBy 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.\nAbout the Role\nWe’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.\nThis 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.\nYou’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.\nJust 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.\nThis 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.\nAs 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.\nOffice Location\nWe 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.\nWe 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.\nLunch 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.\nWhat you will do\nBuild and lead Sprinter’s ML engineering function as the company’s first dedicated ML engineering hire\n\nDefine Sprinter’s ML platform and deployment paradigm across training, serving, features, monitoring, retraining, and governance\n\nMake foundational build-versus-buy, architecture, tooling, and platform decisions that future models and engineers will build on\n\nDesign and build production training and inference pipelines that are reliable, observable, and maintainable\n\nPackage models for deployment and serve predictions through APIs, batch jobs, or other production workflows\n\nBuild clean interfaces between data systems, models, and product systems so ML can be consumed safely and reliably\n\nMaintain feature pipelines and ensure features remain fresh, correct, and consistent between training and serving\n\nImplement monitoring for model performance, drift, data quality, latency, cost, reliability, and production behavior\n\nPrevent training-serving skew, silent degradation, and model regressions before they become production issues\n\nAutomate retraining, validation, deployment, rollback, and other production ML workflows where appropriate\n\nEstablish reproducibility, versioning, model governance, and operational readiness practices as company defaults\n\nPartner with engineering, data platform, product, operations, and applied science teams to productionize models and improve handoffs\n\nWrite design docs, define technical standards, and bring the broader engineering organization along on key ML infrastructure decisions\n\nSet the technical bar for ML engineering by helping interview, mentor, and eventually hire engineers who follow\n\nWhat you have done\nSpent 8+ years building production software, data systems, ML systems, platform infrastructure, or related technical systems\n\nBuilt and owned ML systems in production across training, serving, features, monitoring, and deployment\n\nTaken models from prototype or research stage into reliable, production-grade systems\n\nBuilt or meaningfully scaled ML infrastructure, MLOps platforms, model-serving systems, feature pipelines, or related infrastructure\n\nDesigned systems that other engineers, data scientists, analysts, or product teams rely on\n\nMade architectural decisions around ML platform design, serving patterns, feature infrastructure, build versus buy, and operational standards\n\nWorked with cloud infrastructure, containers, CI/CD, orchestration, data pipelines, and production deployment workflows\n\nBuilt monitoring, observability, validation, or alerting for ML systems, data systems, or high-reliability production services\n\nCreated reproducible workflows across data, features, models, training runs, deployments, or experiments\n\nPartnered closely with data science, applied science, data platform, product, operations, or backend engineering teams\n\nOperated in ambiguous environments where there was no existing playbook and technical decisions had a long half-life\n\nBalanced speed, simplicity, reliability, privacy, and long-term maintainability in production systems\n\nWhat gives you an edge\nYou’ve been an early ML engineer, founding ML engineer, or first ML infrastructure hire at a startup\n\nYou’ve built ML infrastructure in a high-growth or operationally complex environment\n\nYou have depth in large-scale model serving, feature infrastructure, LLM infrastructure, or real-time inference systems\n\nYou have a background in backend engineering, data engineering, MLOps, platform engineering, or infrastructure engineering\n\nYou have experience with feature stores, feature pipelines, or production data systems at scale\n\nYou’ve helped interview, hire, mentor, or set the technical bar for ML engineers, platform engineers, or data engineers\n\nYou’ve worked with healthcare data, PHI, HIPAA-aware systems, or other sensitive data environments\n\nYou have experience with security, privacy, governance, or compliance considerations for production ML systems\n\nWhat makes you successful\nYou decide what the pattern should be and bring the rest of the organization along\n\nYou reach for the simplest system that works, adding complexity only when the value justifies it\n\nYou know what it takes to make a model production-ready and can communicate those requirements clearly\n\nYou are an accelerator for applied science, data, product, and engineering teams, not a gatekeeper\n\nYou build interfaces that make models easy to consume and hard to misuse\n\nYou prevent silent degradation before it becomes an incident\n\nYou create standards that help future engineers move faster\n\nYou raise the technical bar for everyone who joins the function after you\n\nDay to Day\nIn this role, you might spend your time:\nDeciding what Sprinter’s serving and feature paradigms should be and writing the design docs behind those decisions\n\nHardening a training pipeline or batch-inference workflow\n\nProductionizing a model handed off from another team\n\nDebugging a model-serving issue or production data quality problem\n\nReviewing feature freshness, model performance, drift, latency, or cost\n\nBuilding validation and rollback workflows for model deployments\n\nPartnering with product and operations teams to understand how model behavior impacts real-world workflows\n\nInterviewing a candidate, mentoring an engineer, or setting a new technical standard for the ML engineering function\n\nThe Interview Process\nWe aim to complete the interview process within 2-3 weeks. It will usually consist of:\nRecruiter Screen: Background fit, motivation, and compensation alignment\n\nHiring Manager Interview: Technical experience, first-of-function fit, and ML infrastructure depth\n\nHands-on Technical Assessment: Practical ML engineering, production systems, and implementation ability\n\nOnsite Interview: Systems design, technical case study, behavioral interview, and lunch with the team\n\nReferences: Validation of performance, judgment, and working style\n\nWhat we offer\nMeaningful pre-IPO equity\n\nMedical, dental, and vision plans 100% paid for you and your dependents\n\nFlexible PTO + 10 paid holidays per year\n\n401(k) with match\n\n16-week parental leave policy for birthing parent, 8 weeks for all other parents\n\nHSA + FSA contributions\n\nLife insurance, plus short and long-term disability coverage\n\nFree daily lunch in-office\n\nAnnual learning stipend\n\nRelocation assistance\n\nEqual Opportunity Statement\nSprinter 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.\nBeware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers.\nIf 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. 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