# Sr. Staff Machine Learning Systems Engineer **Company**: Hims & Hers **Location**: US - Remote **Work arrangement**: remote **Experience**: senior **Job type**: full-time **Salary**: $240K – $265K **Category**: Engineering **Industry**: Healthcare **Apply**: https://jobs.ashbyhq.com/hims-and-hers/87e928dc-746c-49b2-ba23-06ad6865d0a2?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_650ab4cd-b5c ## Description ## Compensation An estimate of the current salary range for US-based candidates is: $240K – $265K • Offers Equity 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. ## About the Role: We're looking for a Senior Staff engineer who can own the evaluation of AI/ML models end-to-end, ensuring they are effective and safe to ship to patients. This involves setting technical direction for evaluation systems, designing and scaling data pipelines, and leading projects that span teams and quarters. ## You Will: ### Own the evaluation as a whole, not just a slice of it - Set the technical direction for our evaluation systems, including metric, judge and scorer design, and statistical methodology. - Design and scale the data pipelines that feed our models and evaluations. - Proactively address challenges in scaling and complexity of AI evaluation. ### Lead projects that span teams and quarters - Define how we evaluate new AI services from scratch. - Own our approach to adversarial and red-team evaluation as a risk-reduction program. - 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 become reusable standards. - Take vague, cross-team pain points all the way from a rough idea to a fully-specified, shipped system. - Lead major platform improvements and re-architect core systems. ### Grow the people and the network around you - Build real working relationships across ML engineering, data science, platform engineering, clinical, legal, and product. - Become a go-to voice on evaluation methodology and data pipeline design. - Mentor other engineers and help raise the technical and statistical bar of the teams you work with. ## You Have: - 10+ years of experience in ML infrastructure, data engineering, or evaluation/testing systems. - Hands-on depth in evaluation systems, including designing and calibrating LLM judges/scorers. - Hands-on depth in data pipeline engineering, including dataset versioning and feature pipelines. - A history of building things that became the standard approach for others. - Experience leading multi-team projects to completion. - A track record of mentoring other engineers and visibly raising the bar for the teams around you. ## Nice to Have: - Experience with Databricks, MLflow, Unity Catalog, or similar data/eval platforms. - Experience building reporting tools for non-technical teams. - Prior experience in a regulated industry. - A track record of company-wide talks or write-ups that changed how other teams approached a problem. ## Our Benefits - Competitive salary & equity compensation. - Unlimited PTO, company holidays, and quarterly mental health days. - Comprehensive health benefits, including medical, dental & vision, and parental leave. - Employee Stock Purchase Program (ESPP). - 401k benefits with employer matching contribution. - Offsite team retreats ## Skills ### Required - Machine Learning - Data Engineering - Evaluation Systems - Python - Statistical Methodology ### Nice to have - Databricks - MLflow - Unity Catalog - Reporting Tools --- Source: [Apply at jobs.ashbyhq.com](https://jobs.ashbyhq.com/hims-and-hers/87e928dc-746c-49b2-ba23-06ad6865d0a2?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)