Mira Mace — Senior AI Engineer, Voice Systems

Mira Mace — Senior AI Engineer, Voice Systems Type: Full-time | Remote (US), preference for San Francisco or Boston | San Francisco, CA Compensation: $180K–$220K + competitive equity Hiring count: 1 Visa sponsorship: Open to visa transfers (e.g. OPT, H-1B transfers) Reports to: Abhinav Garg, Head of Engineering About Mira Mace Mira Mace pairs Medicare beneficiaries with a dedicated healthcare advocate who navigates appointments, insurance, and care coordination on their behalf — nurses provide the human touch while AI agents handle the tedious backend work, all covered by Medicare. The long-term vision is an AI nurse concierge that makes 24/7 personalized health assistance affordable for everyone, not just the rich or the acutely ill. Founded: 2025 | Team size: 9 | Total funding: $6.5M Industry: Healthcare, AI Website: www.miramace.com Office: Boston (relocating to San Francisco) Backing: Foundation Capital, DefineVC, and top Silicon Valley angels Why Candidates Should Join $1M ARR in under a year: Rapid growth across multiple channels (direct-to-consumer plus B2B referrals from hospitals, insurers, and PCPs), venture-backed by Foundation Capital, DefineVC, and top Silicon Valley angels. Voice is the product, not a feature: Phone conversations are how the work gets done in healthcare — you own the core capability of the company, not a side project. Strong founding team: Google, Meta, Dropbox, and Amazon backgrounds with serial startup experience ranging from bootstrapped ventures to Series D scale-ups. Real moats being built: Reinforcement learning from real nurse interactions, plus local network effects (hospital pharmacy equipment vendor) that compound zip code by zip code, similar to Uber's city-level defensibility. Comp and ownership: Competitive comp ($180K–$220K) plus meaningful early equity, remote-friendly (US) or hybrid in SF/Boston, on a ~9-person team where your decisions define the company's technical DNA. Intake Call Summary Care navigation for Medicare beneficiaries, with a vision of an AI nurse concierge; venture-backed, ~1 year old, at roughly a $1M run-rate revenue. Team of 9 full-time employees — 2 in Boston, the rest distributed across the US; planning to relocate to San Francisco soon. Hiring a Senior AI Engineer focused on reinforcement learning, voice, and agentic systems; core work is automating tasks nurses currently do manually and improving AI accuracy. Open to candidates with 1–3 years of experience, including recent graduates with demonstrated excellence; rapid-learning ability weighted over specific stack experience. Open to remote candidates, but preference for Boston or San Francisco. Ideal profile shows demonstrated excellence (top university or significant achievement) and thrives in unstructured, early-stage environments. Wants to raise the quality of AI automation to reduce reliance on human advocates. Compensation discussed on the call: $200K–$240K, with flexibility for the ideal candidate — note this differs from the $180K–$220K posted salary range (see Source Inconsistencies). The Role Own and improve the agentic systems that automate healthcare-navigation tasks on behalf of patients — building the "cursor for nurses": automating outbound voice calls, agentic search, and multi-agent orchestration, using reinforcement learning from real nurse interactions to continuously improve quality. What You'll Be Doing Automating tasks that healthcare advocates currently do manually — outbound voice calls to insurance, doctors, pharmacies, and patients Building and improving agentic search and multi-agent orchestration systems that coordinate across complex healthcare workflows Designing evaluation infrastructure to measure and improve the quality of AI automations so advocates increasingly rely on them Implementing reinforcement learning loops that use real nurse actions to train and improve models over time Shipping fast and iterating directly with the team that listens to real conversations daily Tech stack: Streaming STT/TTS, telephony (Twilio/SIP), multi-agent orchestration, RAG, reinforcement learning (inferred from the Hard Skills requirements — not stated as an explicit stack in the source). Qualifications Seniority 1–10 years of experience in AI/ML engineering, building systems including voice AI [Required] (see Source Inconsistencies — intake call and role description both say 1–3 years) Work Experience Has shipped voice AI to production [Must have] Shows some kind of excellence in their experience (worked at a growing startup [Series A–D], shipped major products, raised funding, olympiad, etc.) [Required] Ex-YC founder, or from stalling later-stage companies (e.g. Eightfold AI), or Big Tech with prior startup experience [Strongly preferred] Education Undergraduate degree in CS from a top 25 university in the US or Canada [Must have] (role description states this more loosely as "top university" — see Source Inconsistencies) Hard Skills Production voice AI stack — streaming STT/TTS, telephony (Twilio/SIP), latency optimization, endpointing [Required] Agentic systems — context engineering, multi-agent orchestration, tool use, RAG [Required] Built evaluation pipelines for voice systems — metrics, regression suites, production signal [Strongly preferred] Soft Skills Rapid learner who thrives in ambiguity — builds the playbook rather than following one [Required] Miscellaneous Willing to relocate to SF Q1 2027 [Required] (tension with "remote open for strong candidates" — see Source Inconsistencies) Traits to Avoid None specified in source (no "Traits to Avoid" markers present in the HTML). Role Details Salary: $180K–$220K (posted); $200K–$240K discussed on intake call Equity: Competitive (early-stage equity; % not specified) On-site policy: Remote (US); preference for SF or Boston; relocation to SF expected Q1 2027 per the Miscellaneous requirement Visa sponsorship: Open to visa transfers (e.g. OPT, H-1B transfers) Employment type: Full-time Location: San Francisco, CA / Remote (US) Candidate Questions (Paraform submission form) What's one thing in your life where you've demonstrated peak excellence, whether it's academic, professional, athletic, or something else entirely? Describe a production AI agent or voice AI system you've built end-to-end. What was the hardest part of getting it to work reliably in the real world? Are you able to work on-site in San Francisco or Boston, or are you looking for a fully remote role? Scorecard (Paraform submission form) Yes/No per item. Priority labels carried from the JD qualifications. Has shipped voice AI to production [Must have] Undergraduate degree in CS from a top-25 university in the US or Canada [Must have] 1–10 years of experience in AI/ML engineering, building systems including voice AI [Required] Shows some kind of excellence in their experience (growing startup [Series A–D], shipped major products, raised funding, olympiad, etc.) [Required] Production voice AI stack — streaming STT/TTS, telephony (Twilio/SIP), latency optimization, endpointing [Required] Agentic systems — context engineering, multi-agent orchestration, tool use, RAG [Required] Rapid learner who thrives in ambiguity — builds the playbook rather than following one [Required] Willing to relocate to SF Q1 2027 [Required] Ex-YC founder, or from stalling later-stage companies (e.g. Eightfold AI), or Big Tech with prior startup experience [Strongly preferred] Built evaluation pipelines for voice systems — metrics, regression suites, production signal [Strongly preferred] Overall rating (maps to score vs internal JD): Poor fit (<60) · Ok fit (60–74) · Good fit (75–84) · Excellent fit (85+) Interview Process Not available — the interview process ("3 steps") was collapsed in the copied HTML. Expand and re-copy to capture the stages. Stage 1 — TBD Stage 2 — TBD Stage 3 — TBD Offer Extended Candidate Hired Ideal Companies & Backgrounds No dedicated "Ideal Companies" section was present in the source. The only company signals are: Eightfold AI (named as a "stalling later-stage" example), and founding-team pedigree from Google, Meta, Dropbox, and Amazon. If there is a fuller Ideal Companies list on the page, please supply it — I have not fabricated one. Ideal Candidate Profiles For reference only — do not source these specific profiles. Luke (Muwei) G. — LinkedIn AI Software Engineer | Greater Seattle Area Rapid career progression — promoted every year, reaching Staff level quickly Attended McGill (competitive school) and Harvard for master's Worked at Supio, a legal AI startup — relevant agentic/AI experience at an early-stage company Trajectory signals high performance Areas for improvement (per source): Seattle-based and looking for remote (may need location flexibility); $200K–$240K expectation may be at the higher end; reason for leaving Supio unclear. Note: a "View all profiles" link was present — additional example profiles may exist that were not expanded in the copied HTML.