Machine Learning Engineer, Sprinter Health

• 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

Desirable

• 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
• 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.

• We aim to complete the interview process between 2–3 weeks. It will usually consist of:
• Recruiter Screen (30 minutes)
• Hiring Manager Introduction (30 minutes)
• Hands-on-Keys Technical Assessment (1 hour)
• Onsite Interview: Systems Design / Technical Case Study + Research Presentation + Behavioral Interview + Lunch with the Team (4 hours)
• References