Staff Machine Learning Engineer (AI Infrastructure)

TL;DR

Staff Machine Learning Engineer (ML Platform): Building and scaling evaluation and release infrastructure for clinical AI systems with an accent on model quality, observability, and data pipelines. Focus on improving model development velocity, designing large-scale clinical data pipelines, and ensuring system reliability.

Location: Hybrid — San Francisco (3 days onsite)

Salary: $250,000–$300,000 base + equity

Company

An AI healthcare startup building production systems that power clinical AI across millions of patient encounters.

What you will 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.

Requirements

• 5–8+ years in ML engineering or software engineering with an ML focus.
• Strong backend skills in Python, TypeScript, or similar.
• Experience with ML systems, MLOps, or data infrastructure.
• Track record of improving model development velocity or quality.
• Ability to operate across ML, infrastructure, 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.

Culture & Benefits

• Competitive base salary ranging from $250K to $300K.
• Equity ownership in a leading healthcare AI company.
• Hybrid work environment based in San Francisco.