Principal Engineer, Applied AI (Fullstack/Backend)

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

AI is disrupting our stack from the inside, and we're looking for a Principal Engineer to architect that transformation from day one. Applied AI is a small, fast-moving team building a fresh take on how patient care should happen, from intake through ongoing care companionship. While much of the team is focused on research and rapid experimentation, this role is the productionization counterpart — the person who takes what's working and makes it robust, scalable, and reliable enough to run in production at the core of the patient experience.

You'll be the key architect of the patient-facing experience end-to-end, owning how the services across the stack fit together. This is a hands-on role for someone who thinks about robustness, scalability, and debuggability from day one, and who can balance building for today with architecting for where the product needs to go over the next several years.

Key Responsibilities

Own Service Architecture

Own the overall architecture for the Applied AI stack — how individual services blend together into a coherent, end-to-end patient-facing experience, from intake through ongoing companion interactions.

Productionize AI-Driven Experiences

Partner with research-focused teammates who move fast on experimentation, and take their work to production: hardening services for uptime, reliability, and scale. Bring engineering rigor to a team that's optimized for speed of iteration.

Reliability & Operational Excellence

Instill best practices around debugging, observability, and operational readiness. Design for robustness and scalability from day one rather than retrofitting it later.

Mentorship & Talent Growth Mentor senior technical leaders, elevating engineering capabilities across the organization. Champion initiatives that foster talent density, engineering rigor, and a culture of continuous learning

Ambiguity Navigation & Execution Acceleration Lead through complexity and ambiguity, clarifying direction where ownership is unclear. Remove organizational blockers, simplify decision-making, and accelerate delivery of strategic initiatives

Customer-Centric Engineering Translate customer needs and product goals into scalable technical solutions. Define principles and patterns that embed customer obsession into our platform’s architecture

Sustainability & Scalability Design systems and team boundaries to support sustainable growth. Drive investment in non-functional priorities (e.g., observability, migrations, reliability) early in the design process

What You Bring

15+ years of professional software engineering experience with deep architectural expertise and a record of delivering production systems at scale.

Proven track record of influencing technical strategy and delivering cross-org technical initiatives at the group or organizational level.

Excellent track record standing up services at scale in production, with a demonstrated ability to think about robustness, scalability, and debuggability from day one.

Deep experience owning service architecture across a full stack — understanding how services should blend together, not just how to build any one of them well.

A "productionization" mindset: comfortable being the counterweight to a fast-moving research/experimentation culture, focused on uptime and reliability.

Hands-on engineering ability; you want to stay close to the code and the architecture, not just the roadmap.

Some exposure to LLM/AI services — you don't need deep AI expertise, but you should have tested the waters with LLM or AI-powered systems in some capacity.