Staff ML Engineer, AI Platform

What You’ll Own

Eval & Release Infrastructure

• Automated graders and release gates that work across product pods
• Unified eval dataset versioning and execution to replace fragmented workflows
• Production quality monitoring with end-to-end tracing, shared metrics, and automated alerting

Debug Tooling

• Encounter replay that reconstructs exact inference inputs (retrieved chart context, packed prompts, model versions) so teams reproduce issues without digging through logs
• Diff views comparing known-good runs to regressions

Chart Context & Data Pipelines

• The retrieval layer that pulls relevant patient history and assembles it into consistent model-ready inputs
• Feedback loops that capture real-world usage and convert it into training signal
• End-to-end latency instrumentation across every workflow step

Preference Infrastructure

• The system that enables clinician and site-specific behavior across specialties
• Different clinics want different defaults, different phrasing, different workflows. Build the platform that supports customization at scale

Model Serving

• Performance and reliability layer for critical in-house models with clear SLOs, capacity planning, and regression alerts

Who You Are

• 7+ years in software engineering, 3+ focused on ML infrastructure, platform engineering, or data systems
• Staff-level scope: owned cross-cutting infrastructure, influenced technical direction across multiple teams
• Strong backend fundamentals in Python, TypeScript, or similar
• Built eval systems, data pipelines, or ML observability infrastructure
• Comfortable on both the ML and Eng sides of MLOps
• Track record of platform work that measurably accelerated other teams
• In SF, 3x/week in-person

Compensation

Base compensation range of approximately $250,000-300,000 per year, exclusive of equity.