Hims & Hers is the leading health and wellness platform, on a mission to help the world feel great through the power of better health. We are redefining healthcare by putting the customer first and delivering access to care that is affordable, accessible, and personal, from diagnosis to treatment to delivery. No two people are the same, so we provide access to personalized care designed for results. By normalizing health & wellness challenges and innovating on their solutions, we’re making better health outcomes easier to achieve.

Hims & Hers is a public company, traded on the NYSE under the ticker symbol “HIMS.” To learn more about the brand and offerings, you can visit hims.com/about and hims.com/how-it-works . For information on the company’s outstanding benefits, culture, and its talent-first flexible/remote work approach, see below and visit www.hims.com/careers-professionals.

About the Role

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.

You will:

• 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.

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