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Sr. Director of Machine Learning

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Remote from
USA
Salary
USD 330k–360k / yr
Employment
Full Time
Experience
Director
Published
Apply before
28 Nov 2026
Listing views
28
Application actions
3
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AI Summary

The role, at a glance.

Hims & Hers seeks a senior machine-learning executive to lead teams delivering production AI services for clinical intake, provider support, personalization, and operations. The role owns technical strategy across LLM applications, RAG, agentic workflows, reliability controls, custom deep-learning models, and production service performance. This leader will manage ML engineers and applied scientists, establish engineering and responsible-AI standards, and partner closely with clinical, product, security, legal, and compliance stakeholders. Success requires extensive experience shipping and operating scalable ML products, making quality-cost-latency tradeoffs, and leading senior technical talent in a regulated or safety-critical context.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is an executive-level ML leadership role spanning technical strategy, people management, clinical safety, and operational accountability for high-impact AI systems. It requires deep hands-on judgment in both modern LLM systems and custom model development while aligning numerous senior stakeholders.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$345,000
US market range (per year)$280k–$420k
AI insightThe employer discloses a base salary range of $330,000–$360,000 USD per year, with a midpoint of $345,000. This is a credible annual range for a US-based senior director leading applied machine-learning and clinical AI organizations; the estimated broader US annual market range is $280,000–$420,000.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you decide whether to use prompting, fine-tuning, or a custom model for a clinically relevant workflow?

I would begin with the workflow’s risk level, target metric, data availability, and operational constraints. I would establish a baseline using prompting and retrieval, evaluate it against safety, quality, latency, and cost thresholds, and use fine-tuning or a custom model only where evidence shows a durable advantage. For clinical use cases, I would also require documented validation, human oversight, monitoring, and clear fallback behavior.

How have you made LLM-powered services reliable enough for production use?

I treat the model as one probabilistic component in a controlled system. I use structured outputs, validation layers, retrieval quality checks, policy guardrails, deterministic business logic, observability, and graceful fallbacks. I also define service-level objectives for quality, latency, availability, and cost, then use offline and online evaluation to continuously improve performance.

Describe your approach to leading managers and senior technical leaders in an ML organization.

I align leaders around a clear operating model, measurable product outcomes, and explicit ownership boundaries. I create regular mechanisms for technical review, talent development, roadmap decisions, and incident learning while giving experienced leaders meaningful autonomy. My role is to provide strategic context, remove obstacles, raise quality standards, and build a strong succession pipeline.

How would you design evaluation for an AI service supporting healthcare providers?

I would co-design the evaluation with clinical experts, product, engineering, and compliance teams. The framework would include task accuracy, calibration, safety failure modes, subgroup analysis, usability, latency, and escalation rates, with a representative annotated dataset and documented acceptance criteria. Before broad rollout, I would use staged releases, provider feedback, audit trails, and human-in-the-loop controls to validate real-world performance.

Tell us about a time you balanced model quality against latency and cost.

I would frame the decision around the user and business impact of each metric rather than optimizing an isolated benchmark. In practice, I compare model tiers, caching, retrieval strategies, batching, routing, and fallback paths through controlled experiments, then communicate the tradeoffs in business terms. I select the architecture that meets the required quality and safety threshold with sustainable unit economics and operational reliability.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

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.

  • 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.

You have:

  • 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.

  • Bonus: experience in healthcare, digital health, or another regulated domain or with safety-critical ML systems.

  • Bonus: experience with clinical decision support, clinical NLP, or ML systems where a human expert is the end user.

We are committed to building a workforce that reflects diverse perspectives and prioritizes ethics, wellness, and a strong sense of belonging. If you’re excited about this role, we encourage you to apply—even if you’re not sure if your background or experience is a perfect match.

Hims considers all qualified applicants for employment, including applicants with arrest or conviction records, in accordance with the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance, the California Fair Chance Act, and any similar state or local fair chance laws.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Hims & Hers is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please contact us at accommodations@forhims.com and describe the needed accommodation. Your privacy is important to us, and any information you share will only be used for the legitimate purpose of considering your request for accommodation. Hims & Hers gives consideration to all qualified applicants without regard to any protected status, including disability. Please do not send resumes to this email address.

To learn more about how we collect, use, retain, and disclose Personal Information, please visit our Global Candidate Privacy Statement.

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