Mira Mace — Senior AI Engineer, Voice Systems

DavidJoseph&Co

  • San Francisco, California
  • 1 day ago

    Highlights

    You would own and improve the agentic systems that automate healthcare-navigation tasks on behalf of patients, essentially building the "cursor for nurses": automating outbound voice calls, agentic search, and multi-agent orchestration, using reinforcement learning from real interactions to continuously improve quality. A track record with a production voice stack: real-time streaming speech-to-text and text-to-speech, telephony (for example Twilio or SIP), latency tuning, and endpointing.

    Numbers & Facts

    LocationSan Francisco, California

    Description

    San Francisco, CA / Remote (US), preference for SF or Boston · Full-timeCompensation: $180K–$220K + competitive equity

    About the Company

    Our client is an early-stage, venture-backed healthcare AI company building an AI nurse concierge for Medicare beneficiaries. They pair each patient with a dedicated healthcare advocate who handles appointments, insurance, and care coordination, while AI agents take on the tedious backend work, all covered by Medicare. The long-term vision is to make 24/7 personalized health assistance affordable for everyone, not just the wealthy or the acutely ill. The company reached early revenue traction within its first year and is growing quickly.

    Founded 2025 · roughly 9 people · Industry: Healthcare, AI

    The Role

    You would own and improve the agentic systems that automate healthcare-navigation tasks on behalf of patients, essentially building the "cursor for nurses": automating outbound voice calls, agentic search, and multi-agent orchestration, using reinforcement learning from real interactions to continuously improve quality.

    What you'll be doing

    • Automating tasks that healthcare advocates currently do manually, including 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 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

    Requirements

    • Has shipped voice AI to production, not just prototypes or demos
    • 1 to 10 years in AI/ML engineering, including hands-on work on voice systems
    • A track record with a production voice stack: real-time streaming speech-to-text and text-to-speech, telephony (for example Twilio or SIP), latency tuning, and endpointing
    • Strong agentic-systems experience: context engineering, multi-agent orchestration, tool use, and retrieval-augmented generation
    • A clear signal of standout achievement, for example building at a high-growth startup, shipping major products, raising funding, or a comparable distinction
    • A computer science undergraduate degree from a top-25 university in the US or Canada
    • Comfortable operating in ambiguity, the kind of engineer who builds the playbook rather than following one
    • Willing to relocate to San Francisco by Q1 2027

    Nice to Haves

    • Founder experience (for example YC), time at a fast-scaling later-stage company, or big-tech experience paired with an earlier startup chapter
    • Experience building evaluation pipelines for voice systems: quality metrics, regression suites, and production-signal monitoring

    Why Join

    • Voice is the product, not a feature. Phone conversations are how the work gets done in healthcare, so you would own the company's core capability rather than a side project.
    • Join at an early stage on a small team, where your decisions set the technical direction.
    • A founding team drawn from leading technology companies, with repeat-startup experience.
    • Durable moats in progress: reinforcement learning from real interactions, plus compounding local network effects across the care ecosystem.

    Role Details

    • Salary: $180K–$220K
    • Equity: Competitive (early-stage equity)
    • On-site policy: Remote (US); preference for SF or Boston; relocation to SF expected Q1 2027
    • Visa sponsorship: Open to visa transfers (e.g. OPT, H-1B transfers)
    • Employment type: Full-time
    • Location: San Francisco, CA / Remote (US)

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