- Own and optimize pipelines that combine classical ML and LLM-based systems (RAG, scoring, summarization, etc.)
- Fine-tune and evaluate LLMs using both open-source and proprietary data
- Collaborate with product and engineering teams to build real-world applications in healthcare and biopharma
- Implement retrieval strategies, prompt chaining, and inference orchestration for production use cases
- Monitor and improve model quality, latency, explainability, and safety
- Stay ahead of the curve in LLM evaluation, tuning, and agent-based architectures
- 3–6 years of experience in applied ML, with at least 1–2 years working with LLMs
- Strong Python skills and familiarity with ML/LLM frameworks (PyTorch, Transformers, LangChain, LlamaIndex, etc.)
- Comfort with embeddings, vector search, retrieval pipelines, and prompt tuning
- Bias for experimentation, clarity, and pushing things into production — fast
- Understanding of responsible AI practices, model evaluation, and observability
- Experience in healthcare, compliance-sensitive data, or regulated environments
- A chance to make a real impact in the future of healthcare
- Autonomy, ownership, and the ability to chart your own growth path
- Competitive compensation and benefits
- 100% employer-paid health, vision, and dental insurance
- Retirement plans (401k), disability insurance, employee assistance programs
Skills Required
- 3-6 years of experience in applied machine learning
- 1-2 years of experience working with large language models
- Strong Python skills
- Familiarity with machine learning and LLM frameworks such as PyTorch, Transformers, LangChain, or LlamaIndex
- Experience with embeddings, vector search, retrieval pipelines, and prompt tuning
- Understanding of responsible AI practices, model evaluation, and observability
- Experience in healthcare, compliance-sensitive data, or regulated environments
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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.
