Sr. Director of Machine Learning

Hims & Hers•,

9d•$330,000 - $360,000•Remote

About The Position

We're looking for a Sr. Director of Machine Learning to lead the teams building the AI services at the core of the Hims & Hers experience. This leader owns the applied side of our AI portfolio: the LLM-powered services that handle clinical intake, provider support, personalization, and operational workflows, as well as the in-house deep learning models that drive recommendations and predictions where accuracy and clinical soundness matter most. This is a builder's leadership role. You'll set the technical direction for how we turn foundation models and custom models into dependable production services, grow a team of ML modelers and ML production engineers, and partner with our ML platform and evaluation organization to make sure what we ship is measurable, safe, and continuously improving.

Requirements

14+ years of experience in machine learning and software engineering, including 8+ years leading ML teams and experience managing managers or senior tech leads.
A track record of shipping ML-powered products to production at scale — not just prototypes or research — and owning them operationally over time.
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.
Real experience training and deploying deep learning models (recommendation, ranking, classification, or sequence models) where model quality directly affects user or business outcomes.
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.
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.

Nice To Haves

Experience 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.

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.
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.