AI Engineer

Role Description

We're looking for a talented and ambitious machine learning engineer with 5+ years of experience building production AI systems who's excited to solve one of the toughest challenges in healthcare - automatically resolving claim denials. You'll be the technical lead for our agentic AI platform that autonomously researches, understands, and resolves insurance claim denials, helping healthcare practices recover millions in lost revenue.

• Design and build the architecture for our agentic AI system that autonomously resolves insurance claim denials
• Develop specialized AI agents for denial classification, root cause analysis, evidence retrieval, policy reasoning, and appeal generation
• Implement multi-agent orchestration frameworks that coordinate complex workflows across research, decision-making, and document generation
• Create RAG systems to retrieve relevant clinical documentation, billing records, and payer policy information
• Build and optimize evaluation frameworks and feedback loops to continuously improve agent performance and reliability
• Create prompt engineering strategies and fine-tuning approaches to optimize LLM behavior for healthcare billing workflows
• Collaborate with billing managers to understand denial resolution workflows and translate them into agent behaviors
• Collaborate on code reviews and technical design documents to ensure code quality and distribute knowledge

What You'll Do

As a machine learning engineer, you'll design and build the architecture for our agentic AI system that autonomously resolves insurance claim denials. You'll develop specialized AI agents for denial classification, root cause analysis, evidence retrieval, policy reasoning, and appeal generation. You'll implement multi-agent orchestration frameworks that coordinate complex workflows across research, decision-making, and document generation. You'll create RAG systems to retrieve relevant clinical documentation, billing records, and payer policy information. You'll build and optimize evaluation frameworks and feedback loops to continuously improve agent performance and reliability. You'll create prompt engineering strategies and fine-tuning approaches to optimize LLM behavior for healthcare billing workflows. You'll collaborate with billing managers to understand denial resolution workflows and translate them into agent behaviors. You'll collaborate on code reviews and technical design documents to ensure code quality and distribute knowledge.

Requirements

You should have 5+ years of experience in machine learning engineering, with a focus on building production AI systems and deploying models at scale. Strong experience with machine learning frameworks (PyTorch, TensorFlow, or JAX) is required. Experience working with healthcare data or understanding of medical billing workflows is a plus. Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, or JAX) is essential. You should also have proficiency in LLM technologies including prompt engineering, fine-tuning, and RAG systems. You should have experience with vector databases and semantic search systems. Building reliable, production-grade AI systems with proper evaluation and monitoring is a must. You should have experience with MLOps practices including model versioning, A/B testing, and performance tracking. You should have experience working with APIs and integrating AI systems into broader product workflows. You should have a love for your craft and strong technical execution. You should view coding as a craft, not just a task, and you should be excited about using technology to create meaningful impact.

Skills

• Strong experience with machine learning frameworks (PyTorch, TensorFlow, or JAX)
• Experience working with healthcare data or understanding of medical billing workflows
• Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, or JAX)
• Experience with LLM technologies including prompt engineering, fine-tuning, and RAG systems
• Experience with vector databases and semantic search systems
• Experience building reliable, production-grade AI systems with proper evaluation and monitoring
• Experience with MLOps practices including model versioning, A/B testing, and performance tracking
• Experience working with APIs and integrating AI systems into broader product workflows