Senior Director of Machine Learning

Join our team as a Senior Director of Machine Learning, where you will lead and grow a team of ML engineers, applied scientists, and ML production engineers. You will own the strategy for building on top of frontier LLMs, drive the design of harnesses and glue around LLM calls, and direct the development of in-house deep learning and classical ML models. You will also set the bar for how AI services are productionized, partner with Clinical and Medical Affairs, and establish engineering and scientific standards across the team. Additionally, you will build the team's hiring, leveling, and mentorship practices, and act as a senior technical voice in the AI organization.

• Lead and grow a team of ML engineers, applied scientists, and ML production engineers to build AI services from problem framing to production operation.
• Own the strategy for building on top of frontier LLMs, including prompt and context design, retrieval, tool and function calling, and agentic workflows.
• Drive the design of the harnesses and glue around LLM calls, ensuring dependable, auditable outputs inside product and clinical workflows.

• 14+ years of experience in machine learning and software engineering, including 8+ years leading ML teams and experience managing managers or senior tech leads
• Depth in understanding modern LLM application development: RAG, prompt engineering, fine-tuning and adaptation, evaluation, agent and tool-calling architectures, and the practical limits of each
• Comfort operating with ambiguity: taking a vague, high-value problem and turning it into a shipped system with measurable impact
• Excellent communication skills, with the ability to influence peers, executives, and clinical stakeholders
• Real experience training and deploying deep learning models (recommendation, ranking, classification, or sequence models) where model quality directly affects user or business outcomes
• Bonus: experience with clinical decision support, clinical NLP, or ML systems where a human expert is the end user
• Strong software architecture judgment — you can reason about service boundaries, data flow, failure modes, and cost as fluently as you can about model architecture
• Experience balancing model quality against latency, cost, and operational complexity, and making those tradeoffs legible to non-technical partners
• Bonus: experience in healthcare, digital health, or another regulated domain or with safety-critical ML systems
• A track record of shipping ML-powered products to production at scale — not just prototypes or research — and owning them operationally over time