AI that actually understands healthcare is transforming operations, one workflow at a time.
The Role
Lead the development, fine-tuning, deployment, and optimization of machine learning solutions involving LLMs, vision models, and NLP for healthcare applications. Build production-grade models and pipelines, support scalable GPU and TPU training, validate performance and compliance, collaborate with healthcare and product teams, and mentor junior engineers. The role also requires documenting technical approaches and applying MLOps and LLMOps practices in regulated environments.
Summary Generated by Built In
About Autonomize AI
The Opportunity
What You'll Do
Qualifications
Nice to Have
Who You Are
What We Offer
How to Apply
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.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
Send your resume and a brief cover letter to [email protected] explaining why you're the right partner for this mission.
Skills Required
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field
- 5-7 years of experience in machine learning engineering
- Significant experience developing production-grade models and model pipelines in a regulated industry such as healthcare
- Hands-on expertise with large language models, computer vision models, and classic NLP technologies
- Proficiency in Python
- Extensive experience with TensorFlow, PyTorch, OpenCV, or similar machine learning libraries and frameworks
- Strong understanding of deep learning, model fine-tuning, hyperparameter optimization, and model optimization
- Experience deploying and managing machine learning models in production environments
- Working knowledge or experience with MLOps and LLMOps tools such as MLflow and Kubeflow
- Working knowledge of software engineering principles and best practices
- Working knowledge and experience with classic machine learning techniques and frameworks
- Experience with distributed training on GPUs and TPUs
- Knowledge of cloud machine learning platforms such as Azure ML or SageMaker
- Experience deploying NLP or machine learning systems in large or complex organizations
- Experience scaling machine learning model training and inference efficiently
- Experience with Kafka, Spark, Hadoop, or Snowflake
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The Company
What We Do
Autonomize AI Agents & Copilots organize, contextualize and summarize unstructured data to reduce the administrative burden for healthcare knowledge workers to make data-driven decisions and improve patient outcomes. Our customers include health plans, providers and life sciences companies. Unlike generic AI systems retrofitted for healthcare, Autonomize deeply understands medical contexts, terminologies, and operational nuances. Our healthcare-focused AI Agents & Copilots augment knowledge work, drastically reducing administrative burden. Care management teams spend 78% less time per case, achieving an impressive 85% boost in case review efficiency. Prior authorization processes that traditionally take 20-30 minutes shrink to mere seconds, accompanied by an 80% reduction in manual errors, saving millions of dollars annually. Our AI Agents turn chaotic, unstructured healthcare data—clinical notes, PDFs, faxes, and claims—into structured, contextual information that informs decisions and actions. This has driven substantial real-world impact: organizations using Autonomize experience a 92% reduction in manual effort for care gaps and HEDIS chart reviews, dramatically improving compliance and STAR ratings. Autonomize AI is purpose-built for healthcare, transforming healthcare operations one workflow at a time through AI-native solutions that deliver immediate, scalable impact.
