Staff Applied AI Engineer

About the Staff Applied AI Engineer role

We are hiring Staff Applied AI Engineers across a range of seniority levels to push the frontier of applied AI in healthcare. As an MTS on the engineering team, you will design, build, and ship ML and LLM systems that directly shape how clinicians and patients interact with our platform. You will work end-to-end: framing the problem, exploring data, training and evaluating models, productionizing inference, and measuring real-world clinical and product impact. The bar is high and the surface area is large; we are looking for engineers who want significant ownership and are excited to operate where research meets production.

What You'll Do

Define technical strategy across multiple AI initiatives, driving architectural decisions, influencing product and research direction, and aligning engineering investments across teams to maximize long-term business and clinical impact.
Design, build, train, evaluate and improve advanced machine learning and LLM-based systems for patient and provider-facing products (e.g., conversational AI, personalization, user understanding, clinical decision support, chronic care management).
Own problems end-to-end: scope the problem with clinicians and product partners, build datasets and evaluations, iterate on modeling, and ship to production with the right monitoring and guardrails.
Develop robust evaluation frameworks — offline benchmarks, human-in-the-loop review, online experiments — that give us confidence our models are safe, accurate, and improving over time.
Build and improve the platform that lets the team move quickly: data pipelines, training and inference infrastructure, prompt and model management, and tooling for clinical reviewers.
Partner closely with clinicians, product, and engineering to translate medical and operational requirements into ML problems and ship measurable improvements to patient and clinician experience.
Set technical direction for your area, mentor other engineers, and raise the bar on engineering and scientific rigor. The scope of leadership scales with seniority.
Stay close to the literature and the rapidly evolving AI ecosystem; bring back what is most useful for our patients and our team.

What You'll Bring

Bachelor’s degree in Computer Science, Software Engineering, Math, or other related technical degree
5+ years of hands on engineering experience with 2+ years  building and deploying machine learning systems including generative AI (LLMS), and a clear track record of impact.
Strong software engineering fundamentals and the ability to ship reliable, well-tested code in Python (or a comparable language) in a production environment.
Practical understanding of modern LLM techniques: prompting, retrieval-augmented generation, fine-tuning, evaluation, and the trade-offs between them.
Comfortability working with messy, real-world data and designing evaluations to know whether a system is actually working.
Strong written and verbal communication; ability to cross-collaborate with clinicians, product managers, and engineers across disciplines.
A bias toward action and ownership: you can take an ambiguous problem, drive it to a result, and bring others along.
Care for the mission. You want your work to translate into better health outcomes for real patients.
Nice to have
Experience applying ML or LLMs in healthcare, life sciences, or another regulated, high-stakes domain.
Experience with clinical NLP, medical knowledge representation, or working with electronic health record data.
Experience building agentic systems, tool-using LLMs, in production.
Experience scaling ML infrastructure — training pipelines, distributed inference, evaluation platforms — for a small, fast-moving team.
Track record of technical leadership: setting direction across teams, mentoring engineers, or publishing influential work.

What We Offer

High ownership work on problems that matter, with a tight feedback loop from real clinicians and patients.
A small, senior team where your work shows up in the product quickly.
Competitive compensation, meaningful equity, and comprehensive benefits.
Remote-first, flexible work environment across the U.S.