Senior Staff SDET, Customer Engineering

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.

The best SDETs in the industry are no longer measured by how many tests they write. They're measured by the systems they build that decide whether software is safe to ship — and, increasingly, by how well they wield AI to do it.

That's the role we're hiring for. We're looking for a Senior Staff SDET to own quality architecture and engineering strategy for our most critical systems, and to lead how we apply AI across our entire testing and release pipeline. You'll build the frameworks, platforms, and agent-driven workflows that the rest of engineering depends on, define what "safe to ship" means here, and stay deep in the code while doing it.

This is a genuinely hands-on senior technical leadership role with impact that spans many teams. If you want to build AI-native quality systems rather than bolt AI tools onto a legacy suite, this is that job.

• Own quality architecture for our most critical systems and initiatives, defining long-term quality strategy aligned with business and product goals.

• Design shared quality platforms, automation frameworks, and test infrastructure that are adopted across multiple teams.

• Establish quality patterns and guardrails that scale across teams, eliminating duplicated, brittle, or low-signal testing approaches through platform-level solutions.

• Anticipate systemic quality and reliability risks and drive preventative architectural solutions before they impact customers.

• Set the standard for what "great" looks like in quality engineering at scale.

• Define and drive our strategy for AI-assisted testing — autonomous test generation, intelligent test selection, agent-based failure triage, and self-healing automation — with a clear-eyed view of where each genuinely pays off and where it adds noise.

• Build the guardrails that make AI-driven testing trustworthy: knowing when a self-healing agent is masking a real regression versus absorbing harmless implementation drift is the difference between leverage and liability.

• Orchestrate AI agents as part of the CI/CD decision pipeline, turning raw test signal into release confidence rather than more dashboards.

• Own quality for our AI-powered product features — designing eval suites, golden datasets, LLM-as-judge scorers, and statistical approaches for non-deterministic behavior, plus testing for hallucination, safety, and response stability.

• Establish how we test AI-generated application code, validating behavioral correctness and not just syntactic plausibility.

• Evaluate emerging AI testing tooling with real rigor, and be honest about what doesn't work.

• Lead the operational maturity of our quality systems and signals — defining quality metrics, dashboards, and release confidence indicators, and leading post-incident analysis that informs architectural and process improvements.

• Produce exemplary technical artifacts — quality architecture documents, automation frameworks, test platforms — that set the bar for the rest of the team, while staying hands-on in critical quality systems and automation code paths.

• Serve as the accountable technical leader for strategic initiatives, proactively managing reliability, scalability, and technical debt across teams.

• Mentor senior SDETs, quality engineers, and other senior technical leaders — especially on building AI fluency into their practice.

• Establish hiring standards, coaching, and talent development.

• Champion customer-centric quality decisions, ensuring technical trade-offs reflect real user needs and long-term product value.

• Partner with Engineering and Product leadership to embed scalability and resilience into roadmap planning, not just technical design.

• 10+ years of hands-on experience in quality engineering or SDET roles, including experience architecting quality strategy and automation platforms across multiple teams.

• Proven track record designing and scaling test automation frameworks across the full stack — frontend web, mobile, and backend/API (e.g., Playwright, Cypress, Selenium, Appium, Postman, or similar).

• Deep proficiency in one or more programming languages (e.g., JavaScript, TypeScript, Kotlin) used to build and maintain shared automation frameworks and tooling. You write production-quality code, not just test scripts.

• Hands-on experience applying AI to testing — LLM-assisted test generation, agent-based workflows, intelligent test selection, or self-healing automation — with informed opinions about where it delivers and where it disappoints.

• Demonstrated ability to define quality architecture and standards that scale across many teams, including shared quality platforms and test infrastructure.

• Strong grasp of modern development and delivery practices (agile/lean, DevOps, shift-left testing, TDD) and experience embedding quality gates into CI/CD systems (e.g., GitHub Actions, CircleCI, Jenkins).

• Experience mentoring and developing senior quality engineers and senior technical leaders, with a track record of raising the technical bar across a group or organization.

• History of leading post-incident and root-cause analysis that drives architectural and process change.

• Excellent judgment on trade-offs between short-term delivery and long-term system resilience and maintainability.

• Strong executive communication skills, with the ability to influence engineering and product leadership on quality strategy, risk, and investment.

• Experience with performance and load testing at scale.

• Familiarity with LLM observability and evaluation tooling (e.g., Braintrust, Arize, MLflow, LangSmith, or similar).

• Experience with cloud-based testing platforms and tools (e.g., AWS Device Farm, Sauce Labs, BrowserStack).

• Familiarity with e-commerce or healthcare platforms and quality-at-scale practices in regulated environments.

• Experience with internationalization and localization testing.

• A track record of building quality engineering communities of practice — tech talks, katas, lunch-and-learns.

• Published writing, talks, or open-source work on modern testing practice or AI in quality engineering.

• Experience building evaluation or testing infrastructure for LLM-based or ML-driven product features (e.g., eval harnesses, LLM-as-judge scoring, regression testing for non-deterministic systems).