ML Engineer

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.

Hybrid & Office Experience

We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days.

We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most.

Lunch is provided every day, and the entire team takes an hour to eat together. It’s one of the ways we stay connected outside of meetings. You’ll usually find us playing a board game before getting back to work.

What you will do:

Production ML Systems

• 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.

Reliability & Observability

• Monitor drift, data quality, latency, cost, and performance.
• Automate retraining and validation, and design safe rollback.
• Prevent training-serving skew and silent model degradation.

Collaboration & Craft

• Productionize models handed off from other teams.
• Build clean interfaces between data, model, and product systems.
• Implement reproducibility, versioning, and model-governance artifacts..

• 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.

What gives you an edge:

• 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

• 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

What we offer:

• 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