Staff Engineer

Mira Mace pairs Medicare beneficiaries with a dedicated healthcare advocate who navigates appointments, insurance, and care coordination on their behalf. Our customers get the support of caring nurses while AI agents handle the tedious backend work — all covered by Medicare.

Today, 24/7 personalized health assistance is only available to the rich or extremely sick. Our vision is for everyone to be able to afford a health assistant who knows your health history deeply, navigates the healthcare system on your behalf, and propels you to become the healthiest version of yourself.

Our founding team brings a mix of strong technical experience from companies like Google, Meta, Dropbox, and Amazon, along with serial startup experience ranging from early bootstrapped ventures to Series D scale-ups. We are backed by Foundation Capital, DefineVC and top Silicon Valley angel investors.

We're looking for a Staff Engineer to own the technical quality bar for the team. We're at the point where the decisions we make now determine what the next three years cost us: our platform spans real-time voice AI, agentic systems, a web application our team lives in all day, and integrations that carry both patient care and revenue — all moving fast, all built by a small team.

You'd own the view across all of it. That means the architecture and the build-versus-buy calls, where we invest in reliability and where we take on debt deliberately. It also means the engineering harness — the tooling, guardrails, and automation that let people ship in parallel without breaking each other's work, and that let non-engineers ship safely too, with AI doing the work and the system catching the mistakes. And it means being the bar raiser: the engineer whose review makes the work better and whose standards the team adopts and keeps.

This is a hands-on role. You'll write code and review a lot of it, set the standards for testing, observability, security, and deploys, and then be the one who holds the line on them. Like every engineer here, you'll also own business outcomes: take a company goal, find where the leverage is, turn it into a roadmap, ship, and measure whether it moved the number.

The archetype we have in mind: a deep backend or full-stack engineer who has built systems that held up under real load and real consequences, and who is now genuinely comfortable with AI/ML engineering. The mirror image works just as well — a strong ML engineer whose software engineering depth is equally real. Either way, you've led teams before and raised the bar on them.

• Own the technical quality bar. Define what good looks like across the codebase — architecture, testing, observability, security, performance — and make it stick through design review, code review, and your own work.

• Make the architectural calls. Own the decisions that are expensive to reverse: service boundaries, data models, the shape of our AI infrastructure, how systems fail and recover. Write them down so the team knows why.

• Own build vs. buy. Decide what's core and what's a vendor. Evaluate honestly, commit clearly, and revisit when the facts change.

• Build the engineering harness. Own the tooling and automation that let people ship in parallel and reliably — CI, test infrastructure, environments, safe deploys, and AI-assisted development workflows. Push that leverage beyond the engineering team, so non-engineers can ship real changes with the system catching the mistakes.

• Lay the foundation for mission-critical systems. Our platform can't be down or quietly wrong. Build the reliability, monitoring, and safety properties that let us grow by orders of magnitude.

• Go deep where it's hardest. Take on the problems no one else can — the gnarly production incident, the system that has to be redesigned while it's running, the AI subsystem whose behavior nobody can currently explain.

• Raise the team. Mentor engineers, sharpen designs, and make everyone around you better. Multiply the team's output rather than just adding your own.

• Turn goals into a roadmap. Take a company goal, break it into the problems behind it, and sequence the work — weighing risk, cost, and where the technology is heading, for both classical systems and AI. You write the plan, defend the tradeoffs, and change it when the data says to.

• Staff-level experience as a backend or full-stack engineer, with a track record of owning systems that mattered — high traffic, high stakes, or both.

• Real depth in AI/ML engineering. You've built and operated LLM-powered or ML systems in production and can reason about their failure modes, evaluation, cost, and latency as fluently as you reason about a database.

• You've made architectural decisions at scale and lived with the consequences. You can talk concretely about a call you got right and one you got wrong.

• Strong judgment on build vs. buy, on when to invest in infrastructure versus ship the simple thing, and on how much rigor a given system deserves.

• You've set and enforced engineering standards on a team, in a way that made people faster rather than slower.

• You're passionate about being on the cutting edge of software engineering. Best practices are changing fast right now, and you're the person who tracks what's actually working and brings it to the team.

• You've led a team and been its bar raiser, whether or not you carried the manager title.

• You're hands-on. You still write and review code, and you'd be unhappy if you couldn't.

• You have product judgment, and can show it: a time you challenged a spec, cut scope to ship what mattered, changed course after watching real users, or built something nobody asked for that turned out to matter.

Strong ML engineering backgrounds are equally welcome, provided the software engineering depth is there too. We care that both halves are real.

• Experience in healthcare or another regulated industry (HIPAA, PII/PHI handling, auditability).

• Experience with real-time systems, voice, or event-driven architectures.

• Experience as an early engineer at a startup that scaled — you know which foundations mattered and which were premature.

• Python and TypeScript — our stack is FastAPI, Next.js, and Postgres.

Everything we do is guided by a set of leadership principles that define how we operate:

• Patient First. We start with the patient and work backwards; every decision is filtered through whether it makes the patient's life better.

• Sense of Urgency. Every day a patient goes unnavigated is a day someone couldn't get help, so we move fast and decide with conviction.

• Ownership. We own outcomes, not tasks, and never say "that's not my job."

• Insist on the Highest Standards. In clinical quality, operational execution, and how we show up for patients and partners — and we keep raising the bar.

• Question Everything, Unapologetically. We reason from first principles, challenge requirements regardless of who set them, and dig to the root of the problem.

• Mission with massive impact. Every system you build puts a dedicated health advocate in someone's corner. We're building one of the largest AI-first companies in healthcare.

• Define the technical DNA. This role exists to set the bar. The architecture, standards, and harness you put in place now are what the company will run on for years — and what determines how fast everyone else can move.

• Breadth you won't get elsewhere. Real-time voice, agentic AI, and mission-critical integrations in one company — small enough that you can hold all of it in your head.

• Learn fast, build fast. We believe in experimentation, measurement, and steady improvement. You'll ship in days, not quarters.

• Meaningful early equity. Competitive compensation and real ownership in what we're building.