Job Details

Staff Machine Learning Engineer – ML Platform / AI Infrastructure

AI Healthcare Startup | Clinical AI at Scale | Hybrid (San Francisco)

We're hiring a Staff Machine Learning Engineer (ML Platform) to join a leading healthcare AI company building production systems that power clinical AI across millions of patient encounters.

This role sits at the intersection of ML and platform engineering — owning the infrastructure that enables fast, reliable iteration on model quality. You'll build the systems that allow ML teams to ship improvements in days, not weeks.

You'll work on:

• Evaluation systems, release gates, and automated grading
• Observability, tracing, and debugging for ML workflows
• Chart context retrieval and data pipelines for model inputs
• Feedback loops that convert real-world usage into training signal
• Preference systems for clinician-specific model behaviour
• Model serving, performance, and reliability at scale

What You'll Do:

• Build and scale evaluation and release infrastructure for ML models
• Develop tooling to debug, reproduce, and analyze model regressions
• Design data pipelines for large-scale, unstructured clinical data
• Improve latency, reliability, and observability across ML systems
• Enable faster experimentation and iteration across ML teams

What We're Looking For:

• 5–8+ years in ML engineering or software engineering (ML focus)
• Strong backend skills (Python, TypeScript, or similar)
• Experience with ML systems, MLOps, or data infrastructure
• Track record of improving model development velocity or quality
• Comfortable operating across ML, infra, and platform layers

Nice to have:

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

This Role Is:

• Hybrid — San Francisco (3 days onsite)
• $250K–$300K base + equity