Staff AI-Native Engineer Lead

Amae Health•San Francisco, CA

2d•$180,000 - $210,000•Onsite

About The Position

Amae Health is a Series B, venture-backed Public Benefit Corporation delivering specialty outpatient care for people with severe mental illness through high-acuity, in-person clinics nationwide. Our integrated model brings psychiatry, therapy, primary care, dietitians, and community support together under one roof. Leading health systems partner with us to expand this level of care. Alongside care delivery, we are building the science and technology the field has long lacked—proprietary clinical protocols, novel measurement, and an AI-driven data platform designed for severe mental illness. The impact is fewer hospitalizations, greater stability, and more people living meaningful lives in their communities. Engineering excellence drives everything we do, as well as how we scale care. As our Staff AI-Native Engineer Lead, you'll be a systematic thinker about how emerging AI-driven development practices can reshape our entire lifecycle. We don't just use AI; we build our product development processes around it, leveraging AI-native workflows to accelerate architecture, automate testing, and achieve solid reliability. This is a technical leadership role focused on architectural vision and driving engineering excellence through deep expertise. You'll join a focused engineering team building AmaeCare, our product enabling clinicians to provide better care, and AmaeBetter, our mobile app that motivates patients to engage with their care. You won't just ship features; you'll own the reliability, velocity, and architecture of both products end-to-end. This is a high-ownership role at the intersection of product and engineering. You'll partner directly with our CPO on roadmap and requirements, bringing engineering thinking into strategy while translating product vision into technical reality.

Requirements

Extensive experience as a Senior or Staff level software engineer with deep architectural expertise and ability to drive large-scale technical initiatives.
Deep fluency in AI-native development systems and experience building agentic development systems, innovating on systematic, technical improvements to the full development lifecycle.
Proven track record of leading teams that ship reliable, high-quality user-facing products in fast-paced startup environments by balancing velocity and reliability without sacrificing either.
Product mindset. You think like a product person and can help shape requirements (Product management experience is a plus)
Strong communication and influence across technical and non-technical stakeholders: you can carry a room, balance competing input, and push initiatives forward with urgency
Deep full-stack proficiency (React, Next.js, TypeScript, Node.js, SQL/PostgreSQL) with hands-on experience deploying and managing applications on cloud platforms like Vercel or AWS.
Solid understanding of security, authentication/authorization, and data privacy best practices
Degree in Computer Science or equivalent experience
Able to work in-person from San Francisco or New York

Nice To Haves

Familiarity with FHIR, HL7, and healthcare interoperability standards

Responsibilities

Architect and scale AI-native workflows that define our standard development lifecycle.
Institutionalize AI-first development: integrate automated code generation, AI-assisted architecture reviews, and self-healing test suites into our daily engineering operations.
Further the evolution of our engineering culture to be AI-native, continuously measuring and optimizing the impact of AI tools on velocity and code quality.
Own end-to-end engineering delivery and reliability for AmaeCare and AmaeBetter.
Architect shared systems in collaboration with senior engineers to ensure scale.
Drive continuous improvement in systems and processes, enforcing a high bar for production quality while balancing velocity.
Partner with the CPO on product roadmap, requirements, and technical vision.
Collaborate with the Care Delivery team to inform product decisions and ensure alignment with clinical needs.
Translate clinical and user feedback into actionable technical strategy.
Drive adoption of AI-native practices across the team: build conviction, set the pace, and create urgency where it counts.
Run architecture reviews and working sessions that move decisions forward, bringing the room to alignment and keeping momentum high.
Foster a culture where engineers understand the clinical impact of their work.