Senior Machine Learning Engineer( Austin based)

About Autonomize AI

The Opportunity

What You'll Do

• Architect production AI systems. Design and deploy LLM-powered agents, reasoning workflows, and ML pipelines for utilization management, payment integrity, claims, care management, and appeals.
• Lead document intelligence. Set the approach for extracting and reasoning over medical documents, faxes, and healthcare forms using state-of-the-art VLMs, OCR, and structured extraction.
• Drive fine-tuning strategy. Decide when to prompt, retrieve, or fine-tune, and lead SFT, parameter-efficient (e.g., LoRA), and preference/RL-based tuning, including distributed training on GPUs.
• Design retrieval and reasoning systems. Architect RAG pipelines, knowledge graphs, and multi-step agent workflows (e.g., LangGraph) that reason over clinical policies and operational rules.
• Blend classical ML and LLMs. Combine classical ML, NLP, and LLM components into systems that are accurate, cost-efficient, and explainable.
• Own evaluation and accuracy. Define eval strategy, metrics, and golden datasets; lead error analysis; and drive accuracy toward customer targets.
• Build learning loops. Design feedback systems that turn human reviewer corrections and production signals into continuous model improvement.
• Ensure quality, safety, and governance. Make model outputs traceable and auditable, and monitor quality, latency, drift, and safety in production.
• Turn research into a product. Track emerging work in LLMs, agents, and evaluation, decide what's worth adopting, and take it from prototype to production.
• Communicate across audiences. Explain technical trade-offs clearly to product, engineering, clinical experts, leadership, and customers.

Qualifications

• 4+ years in ML engineering, with 2+ years building production LLM or GenAI systems.
• A proven track record shipping production-grade models and pipelines, ideally in healthcare or another regulated industry.
• Deep hands-on expertise with LLMs, vision/VLM models, and classical NLP/ML.
• Strong command of fine-tuning, hyperparameter optimization, and model optimization, including distributed training and efficient inference.
• Hands-on experience with RAG, embeddings, vector search, prompt chaining, tool use, and agent architectures.
• Rigor in evaluation methodology: experimental design, statistical testing, error analysis, and evals for non-deterministic systems.
• Advanced Python skills and fluency with PyTorch, Hugging Face Transformers, and LLM frameworks (LangChain, LangGraph, LlamaIndex).
• Experience with MLOps/LLMOps tooling (MLflow, Kubeflow, Kubernetes) and observability for ML systems.
• Solid software engineering fundamentals: data structures, algorithms, testing, and code quality.
• An understanding of responsible AI, explainability, and working with compliance-sensitive data.
• A BS/MS in Computer Science, Engineering, Data Science, or a related field, or equivalent experience.

Nice to Have

• Healthcare payer domain knowledge (prior authorization, UM, claims, payment integrity, CPT/HCPCS/ICD-10).
• Experience with cloud ML platforms such as Azure ML or SageMaker.
• Experience scaling ML training and inference efficiently.
• Experience with knowledge graphs, graph databases, or self-improving agent systems.
• Experience with big data tools (Kafka, Spark, Snowflake).
• Experience deploying ML/NLP in large or complex organizations.
• Published research, patents, open-source contributions, or technical writing.

Who You Are

• Owner mentality. The buck stops with you. You own it, you learn it, and you get it done.
• Naturally curious. You'd rather experiment than hypothesize. You push boundaries and experiment your way through any problem.
• Passionate and unafraid. You're committed to the team and the mission.
• A team player. You love to learn and win together.
• A strong communicator. You're clear in voice, writing, chat, and video.

What We Offer

• Real-world impact. Your models run in production at some of the largest U.S. health enterprises, shaping decisions in prior authorization, claims, and appeals.
• Category-defining AI products. Help build the AI operating layer for healthcare, a space where the playbook is still being written.
• Hard, unsolved ML problems. Work on VLM document understanding over messy real-world faxes, agentic reasoning over clinical policy, domain fine-tuning (SFT, LoRA, RL), and models that learn from expert feedback in production.
• Significant ownership and autonomy. Help shape AI strategy, not just execute it.
• Modern stack and compute. You'll have GPU resources and access to the latest open-source and frontier models.
• Build your public profile. Publish papers, file patents, write for our tech blog, and speak at conferences.
• Learn with strong peers. Grow through design reviews, paper discussions, and close collaboration with ML engineers and clinical experts.
• 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.

How to Apply