Machine Learning Systems 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.

About the Role:

How do we make advanced AI/ML not just powerful but trustworthy enough to run in a regulated healthcare environment? We're looking for a Senior Staff engineer who can own that question end to end: the data pipelines that feed our models and evaluations, and the evaluation infrastructure — judges, scorers, statistical regression gates, red-team testing — that decides whether AI models are effective and safe to ship to patients.

This is a leadership role for someone who works comfortably across disciplines. You'll set technical direction for how we build, version, and trust the data and judgments that our AI products are evaluated against. Your scope will expand from raw data ingestion and feature/dataset pipelines, through evaluation methodology and statistical rigor, to the reporting surfaces that let clinical and product teams act on what we learn.

You'll spend most of your time on problems that don't have an existing playbook: ambiguous, cross-team, and genuinely hard to reason about. The job is to bring clarity to that ambiguity, chart a path the rest of the team and organization can follow, and see it through from idea to production, building the relationships and buy-in along the way to make it stick.

 

You Will:

Own the evaluation as a whole, not just a slice of it

• Set the technical direction for our evaluation systems; metric, judge and scorer design, the statistical methodology behind regression decisions, and the infrastructure that tracks and categorizes failures over time.

• Design and scale the data pipelines — ingestion, transformation, dataset versioning, labeling and calibration workflows — that both evaluation and downstream data science work depend on.