Machine Learning Engineer (Staff)

Sprinter Health•San Francisco, CA

2d•Hybrid

About The Position

Sprinter Health is seeking a Staff Machine Learning Engineer to be the company's first dedicated ML engineering hire. This role involves building the production systems necessary for training, deploying, monitoring, retraining, and serving machine learning models across the organization. The successful candidate will define the blueprint for ML productionization at Sprinter, covering training and inference pipelines, serving patterns, feature workflows, monitoring, validation, retraining, and model governance. This is a founding, first-of-function role ideal for a hands-on engineer with experience in building ML infrastructure from the ground up, capable of right-sizing solutions for a growing startup. The role requires close collaboration with engineering, data, product, operations, and applied science teams to transform models into reliable systems, serving predictions via APIs and batch jobs, building interfaces between data and product systems, and implementing observability for issues like drift, data quality, latency, and model degradation. Foundational decisions regarding build vs. buy, serving architecture, feature paradigms, deployment standards, and monitoring will be key. As the function grows, there will be opportunities to shape the team, define technical standards, and 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

Been an early ML engineer, founding ML engineer, or first ML infrastructure hire at a startup
Built ML infrastructure in a high-growth or operationally complex environment
Depth in large-scale model serving, feature infrastructure, LLM infrastructure, or real-time inference systems
Background in backend engineering, data engineering, MLOps, platform engineering, or infrastructure engineering
Experience with feature stores, feature pipelines, or production data systems at scale
Helped interview, hire, mentor, or set the technical bar for ML engineers, platform engineers, or data engineers
Worked with healthcare data, PHI, HIPAA-aware systems, or other sensitive data environments
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