ML/LLM Engineer – Applied AI - Autonomize AI - Austin, TX

About Autonomize AI

Autonomize AI is revolutionizing healthcare by streamlining knowledge workflows with AI. We reduce administrative burdens and elevate outcomes, empowering professionals to focus on what truly matters — improving lives. We're growing fast and looking for bold, driven teammates to join us.

The Opportunity

We're looking for a hands-on ML/LLM Engineer who’s excited to ship real-world applications — not just benchmarks. You’ll help us build and optimize AI-native systems that blend structured and unstructured data to power decisions in high-stakes domains. This is a mid to senior-level role for someone who’s ready to go deep on applied ML problems — from retrieval to routing to generation — and ship solutions that deliver impact.

What you'll do

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

Qualifications

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-sens