D
AI Engineer
Discover International
1 hour ago
Full-time
On-site
Senior AI Engineer
Do you have the following skills, experience and drive to succeed in this role Find out below.
Overview 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 xuwmhxw Hands-on fine-tuning (LoRA or similar parameter-efficient methods) in PyTorch / Hugging Face Real-world use of RL or preference optimization on language models Excellent Python and engineering discipline, plus a rigorous approach to evaluation Nice to Haves GPU and distributed training experience Self-hosted model inference Experience in healthcare or another regulated industry Familiarity with common LLM orchestration and experiment-tracking tools
Do you have the following skills, experience and drive to succeed in this role Find out below.
Overview 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 xuwmhxw Hands-on fine-tuning (LoRA or similar parameter-efficient methods) in PyTorch / Hugging Face Real-world use of RL or preference optimization on language models Excellent Python and engineering discipline, plus a rigorous approach to evaluation Nice to Haves GPU and distributed training experience Self-hosted model inference Experience in healthcare or another regulated industry Familiarity with common LLM orchestration and experiment-tracking tools