Machine Learning Engineer
About The Position
At 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. By 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 have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway. We’re looking for an ML Engineer to build the production systems that train, deploy, monitor, retrain, and serve our machine-learning models reliably. You sit between software engineering, data engineering, and modeling, and you make ML work in the real world and stay working. You will build training and inference pipelines, serve predictions through APIs and batch jobs, and stand up the monitoring that catches drift and silent degradation before they reach a patient or a partner. You will turn the models that scientists prototype into systems the company can depend on. The ideal candidate thinks in systems rather than notebooks, knows what a model needs to become production-ready, and builds clean interfaces between data, models, and product.
Requirements
- Strong Python and software-engineering fundamentals.
- Experience with ML frameworks, data pipelines, and model serving.
- Experience taking models from prototype to reliable production.
- Cloud infrastructure, containers, CI/CD, and orchestration.
- Monitoring and observability, plus reproducibility and versioning across data, features, and models.
- Comfort with security and privacy controls for sensitive data.
Nice To Haves
- Background in backend engineering, data engineering, MLOps, or platform engineering.
- Experience with feature stores or feature pipelines at scale.
- Familiarity with healthcare data and PHI-aware systems
Responsibilities
- Build and harden training pipelines.
- Package models for deployment.
- Serve predictions through APIs or batch jobs with reliability in mind.
- Maintain feature pipelines and keep features fresh and correct.
- Monitor drift, data quality, latency, cost, and performance.
- Automate retraining and validation, and design safe rollback.
- Prevent training-serving skew and silent model degradation.
- Productionize models handed off from other teams.
- Build clean interfaces between data, model, and product systems.
- Implement reproducibility, versioning, and model-governance artifacts.
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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
