AI Engineer

Autonomize AI is building production AI systems for healthcare agents and copilots, where models must perform reliably on real-world workflows. This hands-on AI Engineer role focuses on delivering measurable improvements across the full lifecycle, from LLM/VLM and RAG pipelines to evaluation, monitoring, and fast research-to-production execution.

The position is based in Austin, TX and is onsite. You will work with a modern ML stack to ship systems that support utilization management and payment integrity, claims, and appeals.

What you’ll do

• Build and optimize production AI pipelines that combine LLMs with classical ML, including RAG, extraction, scoring, summarization, and classification for utilization management, payment integrity, claims, and appeals.
• Implement VLM- and OCR-based pipelines that convert medical documents, faxes, and healthcare forms into structured, reliable data.
• Run SFT and parameter-efficient fine-tuning experiments (for example, LoRA) on both open-source and proprietary models.
• Develop retrieval strategies, prompt chains, tool-using agents, and inference orchestration (for example, LangGraph) for production use cases.
• Create evaluation harnesses and test sets, conduct error analysis, and translate findings into measurable accuracy gains.
• Track and improve model quality, latency, cost, explainability, and safety for models in production.
• Prototype new techniques from recent research and help determine what is ready to move into production.
• Collaborate with senior MLEs, product, engineering, and domain experts, while documenting work clearly.

Required qualifications

• 2+ years of experience in applied ML and LLMs.
• Strong Python skills and familiarity with PyTorch, Hugging Face Transformers, and LLM frameworks such as LangChain, LangGraph, and LlamaIndex.
• Comfort with embeddings, vector search, retrieval pipelines, and prompt engineering.
• Experience fine-tuning or adapting models, plus working knowledge of classical ML and NLP.
• Understanding of model evaluation, observability, and responsible AI practices.
• Experience deploying models to production, including familiarity with MLOps tooling such as MLflow, Docker, and Kubernetes.
• Solid software engineering fundamentals with clean, testable code.
• A bias for experimentation, clarity, and shipping fast.
• Experience with healthcare, compliance-sensitive data, or regulated environments.
• BS/MS in Computer Science, Engineering, Data Science, or a related field, or equivalent experience.

Technologies you’ll use

• Python, PyTorch, Hugging Face Transformers
• LangChain, LangGraph, LlamaIndex
• Embeddings, vector search
• SFT, LoRA
• MLflow, Docker, Kubernetes

Benefits

• Real-world impact
• Category-defining AI products
• Hard, unsolved ML problems
• Research to production, fast
• Significant ownership and autonomy
• Modern stack and compute
• Build your public profile
• Learn with strong peers
• A high-growth environment with exceptional technical challenges
• Competitive compensation with performance incentives
• 100% employer-paid health, vision, and dental insurance
• Retirement plans (401k), disability insurance, and employee assistance programs

Nice to have

• Experience with VLMs or document AI
• Exposure to healthcare payer workflows such as UM, claims, prior authorization, and medical coding
• Experience with agent frameworks or multi-step reasoning systems
• Open-source contributions, side projects, or technical writing

How you show up

• Owner mentality with a focus on learning and getting it done.
• Curiosity and an experimentation-first approach to problems.
• Commitment to the team and the mission.
• Team-first collaboration and a preference for learning and winning together.
• Clear communication across writing, chat, and video.