Principal Machine Learning Engineer

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.

How can we use vast amounts of proprietary and internet-scale healthcare data to build systems that enable access to significantly better health care? In this role as Principal Machine Learning Engineer, you will define and drive the architectural vision for large-scale, foundational machine learning models and applications that power care personalization, treatment intelligence, and automation at scale. Your work will directly impact every aspect of patient care, including but not limited to MedMatch, our AI-powered system that enhances provider-patient interactions, optimizes treatment recommendations, and expands into new verticals. This impact will be driven by architecting resilient, scalable, and extensible ML platforms that accelerate product development, unlock intelligent care delivery, and influence long-term ML strategy and engineering practices across teams.

• Define and drive the architectural vision for ML and LLM systems that support treatment recommendation, personalization, and intelligent care delivery at scale

• Establish foundational design patterns and infrastructure practices that ensure systems are scalable, resilient, secure, and maintainable

• Work hands-on with Python and ML frameworks like PyTorch to prototype, optimize, and guide implementation of complex model pipelines and platform components

• Align high-impact technical decisions with long-term product strategy by partnering with platform, product, and clinical teams

• Architect and evolve robust ML infrastructure to support continuous training, real-time inference, evaluation, and observability

• Lead cross-functional initiatives that simplify system complexity, improve developer velocity, and increase organizational leverage

• Shape quality strategies across teams by defining standards for testing, observability, performance, and operational risk

• Mentor senior and staff engineers across squads, supporting their growth as systems thinkers and technical leaders

• Champion a culture of sustainable speed, system ownership, and architectural clarity across product and platform teams

• 10+ years of experience in Machine Learning and/or Engineering, with a strong track record of technical leadership and system architecture across teams or business areas

• Expert-level proficiency in Python and advanced ML frameworks such as PyTorch or TensorFlo

• Demonstrated success in designing, scaling, and evolving ML platforms that support production-grade training, deployment, real-time inference, and monitoring

• Deep experience in LLMs and NLP, including transformer models, summarization, conversational agents, and embedding-based retrieval

• Advanced understanding of ML infrastructure, including continuous training, system observability, performance optimization, and platform reliability

• Experience with ML infrastructure tools such as Databricks, MLFlow, SageMaker, or equivalent

• A Master’s degree or PhD in Computer Science, Machine Learning, or a related field (not strictly required)

• Exceptional communication and cross-org leadership skills, with the ability to align diverse technical and non-technical stakeholders around ML strategy and long-term architecture