Staff Applied AI Engineer, Curai Health

• 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
• 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

Desirable

• 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
• 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