Sr Machine Learning Engineer( Austin based)

About Autonomize AI Autonomize AI is an enterprise intelligence platform transforming how healthcare organizations access, orchestrate, and act on complex operational and clinical knowledge. Recognized as a World Economic Forum Technology Pioneer 2026, a Top 100 company on the Inc. 5000, and trusted by three of the five largest U.S. health enterprises, Autonomize AI combines healthcare-specific AI agents, workflow intelligence, and deep domain expertise to enable smarter decision-making across utilization management, care management, claims, pharmacy, and appeals. Backed by Valtruis, Asset Management Ventures, Cigna Group Ventures, ATX Venture Partners, and others. We're building the critical infrastructure for AI-native healthcare operations. We're growing fast and looking for bold, driven teammates to join us. The Opportunity As a Senior Machine Learning Engineer, you'll lead the design, development, and deployment of the ML and AI systems behind our healthcare AI agents and copilots, spanning large language models, vision-language models, and classical ML/NLP. You'll own systems end to end, set technical direction on our hardest problems, and turn the latest research into production systems that hold up in a regulated environment. This role is for someone who ships real-world impact, not just benchmarks, and who raises the bar for the engineers around them. 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…