Job Opportunities API

ABOUT US 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. We've felt the pain ourselves — the endless back-and-forth with insurance, surprise bills, and the lack of clarity when you just need answers. Too many people fall through the cracks, and we're determined to change that. 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. WHAT WE'RE LOOKING FOR We're looking for an AI engineer to build the LLM-powered systems at the core of our product. Our product runs on agents. They decide what work needs to happen for each customer, do a lot of that work autonomously, check the quality of what got done, and assist the people on our team who handle the rest. You'll own that layer. Concretely, you'll build agentic systems that carry multi-step work from start to finish, the evaluation infrastructure that tells us whether they're any good, the retrieval layer that gives them accurate knowledge to reason over, and the AI features our internal users rely on every day. The surface is wide and still being defined, so there's a lot of room to shape what gets built. You'll work directly with the founders. We don't have a strong opinion about which corner of AI engineering you came from — agents, RAG, evals, fine-tuning, retrieval — it all translates. What matters is that you're genuinely fluent with modern LLMs, that you treat them as a backend you can build reliable systems on, and that you've shipped something real to real users. RESPONSIBILITIES - Build agents that do real work. Design and ship LLM-powered systems that take a multi-step task and see it through — deciding what to do, taking action across our systems and third parties, and knowing when to hand off to a human. - Own quality and evaluation. Build the infrastructure that measures how well our AI performs, catches regressions before users feel them, and turns production signal into better prompts, retrieval, and models. Make quality a number we can move, not a vibe. - Build the knowledge layer. Design the retrieval systems our agents reason over, and solve the harder half: keeping that knowledge accurate, versioned, and fresh as the underlying sources change. - Build AI into the product. Ship copilots and assistive features that make our users meaningfully faster, with clear judgment about when the model should act and when it should ask. - Close the loop on real usage. Deploy to production, watch how the systems behave with real users, and improve from what actually happens — in days, not quarters. - Shape the technical roadmap. Work with the founders on what to build next. Bring a sharp sense of what current models can and can't do, and help us make smart bets on where the technology is heading. - Lay the foundation for scale. Make architectural decisions that hold up as usage grows by orders of magnitude, and build with the next engineer in mind. QUALIFICATIONS - You've built LLM-powered systems or agents and shipped them to production — not demos, but systems handling real usage by real people. - Strong overall AI fluency. You can reason concretely about prompting, tool use, context management, retrieval, latency, cost, and failure modes. - You've bu