Senior Applied AI Engineer at Curai — Remote, USA | Base Career

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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.
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.
Bachelor’s degree in Computer Science, Software Engineering, Math, or other related technical degree
3+ years of hands-on engineering experience with 1+ 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.

Senior Applied AI Engineer

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