Machine Learning Engineer (Staff)
Sprinter Health2 months ago
Menlo Park, CA, USA or San Francisco, CA, USAStaff+
Base Salary
$220k - $270k/yr
Responsibilities
- Build and lead Sprinter Health’s ML engineering function as its first dedicated ML engineering hire.
- Define the ML platform and deployment paradigm across training, serving, features, monitoring, retraining, and governance.
- Design and build reliable, observable, maintainable production training and inference pipelines.
- Package models and serve predictions through APIs, batch jobs, and other production workflows.
- Build interfaces between data systems, models, and product systems and maintain consistent, reliable feature pipelines.
- Implement monitoring for model performance, drift, data quality, latency, cost, reliability, and production behavior.
- Automate retraining, validation, deployment, rollback, reproducibility, versioning, and other production ML workflows.
- Partner with engineering, data platform, product, operations, and applied science teams to productionize models.
- Write design documents, establish technical standards, mentor engineers, and help interview and hire future team members.
Requirements
- 8+ years building production software, data systems, ML systems, platform infrastructure, or related technical systems.
- Experience building and owning production ML systems across training, serving, features, monitoring, and deployment.
- Experience taking models from prototype or research stage into reliable production systems.
- Experience building or scaling ML infrastructure, MLOps platforms, model-serving systems, feature pipelines, or related infrastructure.
- Experience designing systems relied on by engineers, data scientists, analysts, or product teams.
- Experience making architectural decisions involving ML platforms, serving patterns, feature infrastructure, build-versus-buy choices, and operational standards.
- Experience with cloud infrastructure, containers, CI/CD, orchestration, data pipelines, and production deployment workflows.
- Experience building monitoring, observability, validation, or alerting for ML, data, or high-reliability production systems.
- Experience creating reproducible workflows across data, features, models, training runs, deployments, or experiments.
- Experience partnering with data science, applied science, data platform, product, operations, or backend engineering teams.
- Ability to operate in ambiguous environments and balance speed, simplicity, reliability, privacy, and maintainability.
- Preferred experience as an early, founding, or first ML infrastructure hire; with large-scale model serving, feature infrastructure, LLM infrastructure, real-time inference, healthcare data, PHI, HIPAA-aware systems, security, privacy, governance, or compliance.
Benefits
- Hybrid schedule in the Bay Area with offices in San Francisco and Menlo Park; employees work from the office Monday through Thursday and from anywhere on Fridays.
- Meaningful pre-IPO equity.
- Medical, dental, and vision plans fully paid for the employee and dependents.
- Flexible PTO and 10 paid holidays per year.
- 401(k) with match.
- 16 weeks of parental leave for birthing parents and 8 weeks for all other parents.
- HSA and FSA contributions.
- Life insurance and short- and long-term disability coverage.
- Free daily in-office lunch.
- Annual learning stipend and relocation assistance.
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