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Sr Machine Learning Engineer( Austin based)
Job Location:
Job Location:
Austin, TX - USA
Monthly Salary:
Not provided by the employer
Monthly Salary:
Posted:
12 November 2026 (6 hours ago)
Posted:
Application Deadline:
9 February 2027
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
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. Were building the critical infrastructure for AI-native healthcare operations. Were growing fast and looking for bold driven teammates to join us.
The Opportunity
As a Senior Machine Learning Engineer youll 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. Youll 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 Youll 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 whats 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. Youd rather experiment than hypothesize. You push boundaries and experiment your way through any problem.
- Passionate and unafraid. Youre committed to the team and the mission.
- A team player. You love to learn and win together.
- A strong communicator. Youre 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. Youll 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
Send your resume and a brief cover letter to explaining why youre the right partner for this mission.
Required Experience:
Senior IC
