Sr. Staff 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.

• Proactively address challenges in scaling and complexity AI evaluation.

Lead projects that span teams and quarters

• Define how we evaluate any new AI service from scratch. Drive multi-team initiatives like replacing manual, inconsistent review processes with statistically sound, automated gates.

• Own our approach to adversarial and red-team evaluation as a risk-reduction program, designing the test suites and failure taxonomies that catch safety and edge-case issues before they reach patients.

• Work through complex, cross-team technical disagreements and drive alignment across engineering, product and AI leaders.

Turn hard, ambiguous problems into solutions other teams can build on

• Originate new approaches and methodology that becomes a reusable standard rather than a one-off fix.

• Take vague, cross-team pain points ("we do this manually and it's inconsistent") all the way from a rough idea to a fully-specified, shipped system, without needing to hand off any part of the journey.

• Lead major platform improvements; re-architecting core systems, removing brittle logic, modernizing how things run with impact that's felt org-wide, not just on your own team.

Grow the people and the network around you

• Build real working relationships across ML engineering, data science, platform engineering, clinical, legal, and product, the kind of trust that gets you looped in early, before decisions are locked in.

• Become a go-to voice on evaluation methodology and data pipeline design: share what you've learned in internal talks, write things up so other teams can use them, and expect your ideas to shape how others approach similar problems.

• Mentor other engineers, including experienced on