Senior Software Engineer, Agentic AI
New
C
Curai HealthHealthcare AI
Remote-first, flexible work environment across the U.S.Full-TimeSenior
Salary175,000 - 210,000 USD per year
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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 their trade-offs.
- Comfort working with messy, real-world data and designing evaluations to determine whether a system is working.
- Strong written and verbal communication and ability to collaborate across clinical, product, and engineering disciplines.
- Ability to take ownership of an ambiguous problem and drive it to a result.
- Experience 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, such as training pipelines, distributed inference, or evaluation platforms, is a plus.
- A track record of technical leadership, mentoring, or influential publications is a plus.
Responsibilities
- Lead technical execution and delivery of complex AI initiatives within a product or technical domain.
- Design, build, train, evaluate, and improve machine learning and LLM systems for patient- and provider-facing products.
- Scope problems with clinicians and product partners, build datasets and evaluations, iterate on models, and ship systems to production with monitoring and guardrails.
- Develop offline benchmarks, human-in-the-loop reviews, 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 in partnership with clinicians, product, and engineering.
- Set technical direction for the area, mentor engineers, and promote engineering and scientific rigor.
- Track relevant research and developments in the AI ecosystem.
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