Machine Learning Engineer (Staff)

Sprinter Health • San Francisco, CA

4d • $220,000 - $270,000 • Hybrid

About The Position

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.

Requirements

• 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

Nice To Haves

• 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

Responsibilities

• 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

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed