We ground our approach in rigorous research, continuous learning, and a deep commitment to clinical integrity. We focus on making a real impact: improving health outcomes, expanding access to care, and setting new standards for what trustworthy, patient-centered healthcare is. Read some of our latest publications.

We are hiring Members of Technical Staff (MTS) across a range of seniority levels — from senior individual contributors through staff and principal — to push the frontier of applied AI in healthcare. As an MTS on the AI/ML 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 Will Do

•     Design, build, train, evaluate and improve advanced machine learning and LLM-based systems for 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 we are looking for

•     Hands-on experience building and deploying machine learning systems including  generative AI (LLMS) , — and a clear track record of impact.

•     Practical understanding of modern LLM techniques: prompting, retrieval-augmented generation, fine-tuning, evaluation, and the trade-offs between them.

•     Comfort working with messy, real-world data and designing evaluations to know whether a system is actually working.

•     Strong written and verbal communication; ability to 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.