Senior Software Engineer, Agentic AI
New
C
CuraiHealthcare AI
Remote-first, flexible work environment across the U.S.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3+ years of hands-on engineering experience with 1+ year building and deploying machine learning systems including generative AI (LLMS)
- Required Skills
- PythonMachine LearningLLMGenerative AI
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, Math, or another related technical field.
- 3+ years of hands-on engineering experience.
- 1+ year building and deploying machine learning systems, including generative AI (LLMs), with a clear track record of impact.
- Strong software engineering fundamentals and ability to ship reliable, well-tested production code in Python or a comparable language.
- Practical understanding of prompting, retrieval-augmented generation, fine-tuning, evaluation, and the trade-offs between these techniques.
- Comfort working with messy, real-world data and designing evaluations to determine whether a system is working.
- Ability to collaborate with clinicians, product managers, and engineers across disciplines.
- Experience applying ML or LLMs in healthcare, life sciences, or another regulated, high-stakes domain is a plus.
- Experience with clinical NLP, medical knowledge representation, or electronic health record data is a plus.
- Experience building production agentic systems or tool-using LLMs is a plus.
- Experience scaling ML infrastructure, including training pipelines, distributed inference, or evaluation platforms, is a plus.
- Technical leadership experience, such as setting direction across teams, mentoring engineers, or publishing influential work, is a plus.
Responsibilities
- Lead the technical execution of complex AI initiatives and own solution design and delivery within a product or technical domain.
- Design, build, train, evaluate, and improve machine learning and LLM-based systems for patient- and provider-facing products.
- Scope problems with clinicians and product partners, build datasets and evaluations, iterate on modeling, and ship systems to production with monitoring and guardrails.
- Develop offline benchmarks, human-in-the-loop review, and online experiments to evaluate model safety, accuracy, and improvement.
- Build and improve data pipelines, training and inference infrastructure, prompt and model management, and tooling for clinical reviewers.
- Translate medical and operational requirements into ML problems with clinicians, product, and engineering partners.
- Set technical direction, mentor engineers, and support engineering and scientific rigor.
- Stay current with AI research and the evolving AI ecosystem and identify useful developments for the team and its patients.
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