Senior Software Engineer, Agentic AI

At Curai, we believe that access to high-quality healthcare is a fundamental human right, not a privilege. Our mission is to radically transform healthcare delivery by harnessing the power of artificial intelligence and clinical expertise to make care more affordable, accessible, and effective for everyone. Patients interact with advanced AI systems at every step of their care and follow-up. Licensed physicians review each case with the patient for the clinical decision in our integrated virtual clinic.

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.

About The Senior Software Engineer, Agentic AI Role

We are hiring a Full-Stack Senior Software Engineer across a range of seniority levels to push the frontier of agentic / applied AI in healthcare. As a member of the technical staff on the artificial intelligence engineering team, you will design, build, and ship ML and LLM systems (agentic AI workflows) 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

• Lead the technical execution of complex AI initiatives, owning the design and delivery of solutions within a product or technical domain while partnering with senior engineers on broader architectural direction

• 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