Senior AI Engineer — Voice

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 an engineer to own our voice AI end to end: the calling pipeline, the quality of every conversation that runs through it, and what we build next. Engineers take a company goal, find where the leverage is, turn it into a roadmap, ship, and measure whether it moved the number.

Our problem space runs on the phone. Getting a customer set up takes a real conversation, and much of the work we do on their behalf can only happen by calling institutions — which means automated phone menus, long holds, transfers, and getting a specific outcome out of someone who didn't expect the call. Many of our customers are older and expect to be spoken with, not processed. Voice AI has to clear that bar, and clearing it is the product.

You'll own the full real-time stack and the systems that make it measurably better with every release: evaluation for calls, replayable regression suites, and the tooling that tells us why a call went wrong before a customer does. You'll work directly with the founders and with the team that listens to these calls every day.

This role is specifically for someone with real voice experience. If you've shipped a production real-time voice agent, we want to talk.

• Turn goals into a roadmap. Take a goal like "more of the people who sign up get through their first call" or "more insurer calls end with the answer we needed," break it into the problems behind it, size them, and decide what to build first. You write the plan, defend the tradeoffs, and change it when the data says to.

• Own the voice pipeline. Build and operate the real-time stack — telephony, streaming speech-to-text, LLM orchestration, text-to-speech, endpointing, barge-in, and the latency budget that keeps a conversation feeling human.

• Drive call quality relentlessly. Define what a good call is, measure it, and move the number. Build the evaluation harness — replayable audio and transcripts, regression suites, per-turn scoring — so quality changes are provable rather than anecdotal.

• Handle the real world. Automated phone menus and DTMF, holds and transfers, voicemail, dropped calls and retries, noisy lines, accents, interruptions, and speaking clearly with an older audience.

• Scale calling volume reliably. Make the system hold up across thousands of calls a day, with clean handoffs to a human when the agent should stop.

• Close the loop with the people who hear every call. Turn what our team observes on real conversations into prompt, model, and pipeline changes, fast.

• Ship fast and iterate with real users. Deploy to production, listen to real conversations, and improve from them — in days, not quarters. Success is the business metric the work was meant to move, not the ship date.

• Lay the foundation for scale. Make architectural decisions in the voice stack that hold up from hundreds of calls a day to hundreds of thousands.
  

• You've built and shipped real-time voice AI to production — not a demo, but a system that took real calls with real people.

• Hands-on depth across the voice stack: streaming speech-to-text, text-to-speech, turn detection and endpointing, barge-in, and latency optimization under a real-time budget.

• Experience running telephony in production (SIP/PSTN, Twilio or similar) and the failure modes that come with it.

• You've built evaluation for voice or conversational systems.

• You've debugged AI systems in production and can trace a bad outcome through audio, transcript, and model behavior to the actual cause.

• 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.

• Experience with real-time voice agent frameworks.

• Speech model fine-tuning, custom vocabularies, or domain adaptation for specialized terminology.

• Experience building agents that navigate automated phone systems on the other end of the line.

• Experience in healthcare or another regulated industry (HIPAA, PII/PHI handling, call recording and consent).
  

• Experience as founder / founding team member / early stage startup.

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 call your system handles is someone who got the help they needed instead of falling through the cracks. We're building one of the largest AI-first companies in healthcare.

• Voice is the product. This isn't a feature team. Phone conversations are how the work gets done in this business, and the quality of them is the company's core capability. You own it.

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

• Grow with us. You'll be part of the team that takes Mira Mace from early product to scale. The decisions you make now will define the company's technical DNA.

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