AI Engineer

This well-funded, growth-stage healthcare technology company pairs healthcare expertise with AI to remove waste from the U.S. healthcare system. Its generative AI is already live, helping insurers find costly errors in medical payments. The next stage is making that AI more accurate, more explainable, and more reliable, and they want an engineer who has done it before. The team is lean, decisions move fast, and engineers work directly with leadership.

Responsibilities

Making models smarter:

adapt and fine-tune large language models on specialized healthcare data, and own the training data, runs, and versioning

Improving learning from experts:

turn feedback from in-house medical reviewers into training signal using preference-based methods (DPO, RLHF, reward models)

Grounding answers in evidence:

build retrieval systems so every AI conclusion can be traced back to the document it came from

Building autonomous workflows:

design AI agents that plan, call tools, and reason through multi-step reviews

Proving it works:

set up the metrics and test suites that show whether a change actually helped

Shipping it:

work with engineering and infrastructure teammates to run everything securely, quickly, and cost-effectively on sensitive patient data

Qualifications

3+ years building ML or AI systems that real users depend on

Production experience with LLM-based agents and retrieval-augmented generation

Hands-on fine-tuning (LoRA or similar parameter-efficient methods) in PyTorch / Hugging Face

Real-world use of RL or preference optimization on language modelsExcellent Python and engineering discipline, plus a rigorous approach to evaluation

Nice to Haves

GPU and distributed training experience

Experience in healthcare or another regulated industry

Familiarity with common LLM orchestration and experiment-tracking tools

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