Senior Director of Machine Learning

Hims & Hers; United States; Remote; USD 200,000 - 260,000; Full time

Benefits offered by this job

Generous PTO

Healthcare coverage

401(k) matching

Remote-first

Competitive salary bands

Employee discounts

Cell data stipend

HSA / FSA options

Job summary

Hims & Hers is seeking a senior ML leader to guide the design, development, and deployment of AI services across clinical and product workflows. You will own LLM strategy, build production-grade models, and drive measurable impact while partnering with clinical stakeholders and cross-functional teams.

You will oversee a team of ML engineers, scientists, and production engineers, shaping multi-year roadmaps, evaluating build-vs-fine-tune-vs-prompt decisions, and ensuring responsible AI practices.

Qualifications

• 14+ years of experience in machine learning and software engineering.
• 8+ years leading ML teams and managing managers or senior tech leads.
• Depth in modern LLM application development: RAG, prompt engineering, fine-tuning, evaluation, agent and tool-calling architectures.
• Comfort operating with ambiguity and shipping measurable impact.
• Excellent communication skills with executives and clinical stakeholders.
• Experience training and deploying deep learning models where outcomes matter.
• Bonus: healthcare, digital health, or safety-critical ML experience.
• Track record of shipping ML-powered products to production at scale and owning them operationally.

Responsibilities

• Lead and grow ML engineers, applied scientists, and ML production engineers.
• Own the strategy for building on frontier LLMs: prompts, retrieval, tools, and workflows.
• Design harnesses around LLM calls: orchestration, guardrails, and deterministic scaffolding.
• Develop in-house deep learning and classical ML models for clinical and recommendation use cases.
• Set production standards: SLOs for accuracy, latency, cost; governance and ownership.
• Decide when to build, fine-tune, or prompt, in response to evolving model capabilities and pricing.
• Partner with Clinical and Medical Affairs to ensure oversight and human-in-the-loop design.
• Collaborate with ML infrastructure to define evaluation criteria and accountability metrics.
• Translate ambiguous business problems into scoped, high-leverage ML work.
• Establish engineering and scientific standards across the team and mentorship practices.

Skills

ML leadership

Team management

Prompt engineering

RAG systems

Model evaluation

Production ML

Communication

Healthcare ML

Job description

• 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 leadsDepth in understanding modern LLM application development: RAG, prompt engineering, fine-tuning and adaptation, evaluation, agent and tool-calling architectures, and the practical limits of eachComfort operating with ambiguity: taking a vague, high-value problem and turning it into a shipped system with measurable impactExcellent communication skills, with the ability to influence peers, executives, and clinical stakeholdersReal experience training and deploying deep learning models (recommendation, ranking, classification, or sequence models) where model quality directly affects user or business outcomesBonus: experience with clinical decision support, clinical NLP, or ML systems where a human expert is the end userStrong software architecture judgment — you can reason about service boundaries, data flow, failure modes, and cost as fluently as you can about model architectureExperience balancing model quality against latency, cost, and operational complexity, and making those tradeoffs legible to non-technical partnersBonus: experience in healthcare, digital health, or another regulated domain or with safety-critical ML systemsA track record of shipping ML-powered products to production at scale — not just prototypes or research — and owning them operationally over time