Senior Director of Machine Learning

• 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
• Direct development of in-house deep learning and classical ML models where a custom model outperforms a general-purpose one, including clinical and recommendation use cases such as medication and treatment-plan recommendations surfaced to providers
• 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
• Make build-versus-fine-tune-versus-prompt decisions deliberately, and revisit them as model capabilities and pricing shift
• 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
• Work with Product, Data Science, Engineering, Security, Legal, and Compliance to translate ambiguous business and clinical problems into scoped, high-leverage ML work
• Establish engineering and scientific standards across the team: experiment design, model documentation, reproducibility, code quality, and responsible AI practices
• 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

Benefits

• Generous PTO: Take the time you need, when you need it - including generous parental leave
• Full healthcare: High-coverage medical, dental & vision coverage for individuals and families
• Retirement planning: Take advantage of our 401(k) plan including contribution matching
• Work from anywhere: We are a remote-first company, so you can work from anywhere you like in the uS
• Robust compensation: We offer competitive salary bands and stock options
• Employee discount: Employees can take advantage of product discounts
• Utility stipend: A extra $75 each month to cover extra cell phone, internet, or data usage
• Spending accounts: Options for additional HSA and FSA plans to help toward healthcare costs

• 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