Sr. Director of Machine Learning

Responsibilities

• Lead and grow ML engineers, applied scientists, and ML production engineers building AI services end to end — from problem framing through production operation.
• Own the strategy for how we build on top of frontier LLMs: prompt and context design, retrieval, tool and function calling, agentic workflows, structured output reliability, fallback and degradation behavior, latency and cost management.
• Drive the design of the harnesses and glue around LLM calls — orchestration, validation, guardrails, deterministic scaffolding — so probabilistic components produce dependable, auditable outputs inside product and clinical workflows.
• Set the bar for how AI services are productionized: SLOs for accuracy, latency and cost, graceful failure, rollout strategy, and clear ownership of production behavior.
• Partner with Clinical and Medical Affairs to ensure clinically-facing models are developed with appropriate oversight, validation, and human-in-the-loop design; ensure providers stay in control of clinical decisions.
• Partner closely with the ML infrastructure and evaluation team members to define evaluation criteria, feedback loops, and annotation needs for every service your team ships — and to hold your team accountable to the resulting metrics.
• Build the team's hiring, leveling, and mentorship practices; develop senior individual contributors and managers.
• Act as a senior technical voice in the AI organization, shaping multi-year roadmap and investment decisions and representing AI strategy to executive leadership.

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