Sr. Director of Machine Learning
Hims & Hers · Remote · US Remote ·
COMPENSATION
$330k – $360k
Listed on the employer's job board (Ashby)
- Sr. Director level
- Fully remote
- LLM & Deep Learning
- Healthcare domain
- Manage managers
Lead the applied AI portfolio at Hims & Hers, overseeing teams that build LLM services and deep learning models. You will define technical direction for productionizing foundation models, ensuring they are safe, measurable, and clinically sound.
This builder's role involves growing a team of ML engineers and scientists while partnering with platform and evaluation groups. You will drive strategy for prompt design, agentic workflows, and custom model development to enhance patient and provider experiences.
What you'll do
- Lead ML engineers and scientists building AI services end to end
- Define strategy for LLM applications including RAG and agentic workflows
- Design orchestration and guardrails for dependable probabilistic outputs
- Direct development of custom deep learning models for clinical use cases
- Set production standards for accuracy, latency, cost, and failure handling
- Partner with Clinical Affairs to ensure appropriate oversight and validation
- Collaborate with infrastructure teams to define evaluation criteria and metrics
- Establish engineering standards for experiment design and code quality
- Build hiring and mentorship practices for senior contributors and managers
What you'll need
- 14+ years in machine learning and software engineering
- 8+ years leading ML teams and managing managers or tech leads
- Proven track record of shipping ML products to production at scale
- Deep understanding of modern LLM application development and limits
- Experience training and deploying deep learning models for business outcomes
- Strong software architecture judgment regarding service boundaries and data flow
- Ability to balance model quality against latency, cost, and complexity
- Excellent communication skills to influence executives and clinical stakeholders
Nice to have
- Experience in healthcare, digital health, or regulated domains
- Background with safety-critical ML systems
- Experience with clinical decision support or clinical NLP
- Work with ML systems where human experts are end users
Stack
- LLMs
- Deep Learning
- RAG
- Prompt Engineering
- Machine Learning
How you'll work
- Fully remote work approach
- Flexible work environment