Sr. Director of Machine Learning

Role overview

This senior leadership role directs applied machine learning across a consumer health platform, owning both LLM-powered services and custom deep learning models. The position combines technical strategy with people leadership, focused on turning foundation models and bespoke models into dependable, production-grade services used in clinical intake, provider support, personalization, and operations. It is a builder-oriented leadership role rather than a pure research position, with direct influence over multi-year AI roadmap and investment decisions.

Responsibilities

- Lead and scale a multidisciplinary organization of ML engineers, applied scientists, and production engineers, taking projects from problem framing through production operation - Set strategy for building on top of frontier LLMs, including prompt and context design, retrieval, tool and function calling, agentic workflows, structured output reliability, fallback behavior, and latency and cost management - Design orchestration, validation, guardrails, and deterministic scaffolding so probabilistic components produce auditable outputs inside product and clinical workflows - Direct development of in-house deep learning and classical ML models where custom approaches outperform general-purpose ones, including recommendation systems surfaced to providers - Establish production standards: SLOs for accuracy, latency, and cost; rollout strategy; graceful failure handling; and clear ownership of production behavior - Partner with clinical, medical affairs, security, legal, compliance, product, data science, and engineering teams to translate ambiguous problems into scoped ML work - Build hiring, leveling, and mentorship practices while developing senior individual contributors and managers

Requirements

- 14+ years of experience in machine learning and software engineering, including 8+ years leading ML teams and managing managers or senior tech leads - A track record of shipping ML-powered products to production - Hands-on expertise with LLM techniques including prompting, retrieval-augmented generation, fine-tuning, and tool-calling architectures, and an understanding of their practical limits - Real experience training and deploying deep learning models such as recommendation, ranking, classification, or sequence models where model quality directly affects outcomes - Strong software architecture judgment covering service boundaries, data flow, failure modes, and cost - Experience balancing model quality against latency, cost, and operational complexity, and communicating those tradeoffs to non-technical partners - Comfort operating with ambiguity, taking a high-value problem and delivering a shipped system with measurable impact - Excellent communication skills with the ability to influence peers, executives, and clinical stakeholders

Nice to have

- Background in healthcare, digital health, or another regulated domain or with safety-critical ML systems - Experience with clinical decision support, clinical NLP, or ML systems where a human expert is the end user