Lead Machine Learning Engineer - ML Platform / AI Infrastructure

About

Join an innovative healthcare AI startup that is transforming clinical AI at scale! We are looking for a Lead Machine Learning Engineer (ML Platform) to advance our mission of building robust production systems that impact millions of patient encounters.

This crucial role combines machine learning and platform engineering, driving the infrastructure needed for rapid and reliable model enhancement. You'll develop systems that empower our ML teams to deliver updates in days rather than weeks.

Responsibilities

• Creating state-of-the-art evaluation systems, automated grading, and release mechanisms
• Implementing observability and debugging tools tailored for ML workflows
• Designing data pipelines that optimize chart context retrieval for model inputs
• Establishing feedback loops to convert real-world usage data into valuable training signals
• Developing preference systems that cater to clinician-specific behaviors of AI models
• Ensuring robust model serving, performance, and reliability at scale

Key Contributions

• Build and scale infrastructure for evaluating and releasing ML models efficiently
• Create advanced tooling for debugging, reproducing, and analyzing model regressions
• Design data pipelines capable of handling large-scale, unstructured clinical data
• Enhance latency, reliability, and observability of our ML systems
• Facilitate faster experimentation and iterations across multiple ML teams

Qualifications

• 5-8+ years of experience in ML engineering or software engineering with a focus on ML
• Strong backend programming skills (Python, TypeScript, or similar)
• Experience in ML systems, MLOps, or data infrastructure
• A proven track record of boosting model development efficiency or quality
• Comfort and expertise operating across ML, infrastructure, and platform layers

Additional Qualifications

• Experience with ML evaluation systems or observability tools
• Background in healthcare or knowledge of regulated environments
• Experience with large-scale retrieval systems or long-context handling

Work Environment

• Hybrid - San Francisco (3 days onsite)
• Compensation: $250K-$300K base salary + equity