Everi Labs accelerates the complete documentation workflow for specialty clinics, replacing work that is still done manually across disconnected tools. As an ML Research Engineer you will invent the methods behind document understanding, cross-document reasoning and compliance validation, and ship them to production. Working on real high-stakes clinical data, you will see exactly where today's models break down, and get to solve problems clinics depend on every day.
Core responsibilities may include
- Invent novel ML methods for document understanding, cross-document reasoning and compliance validation, and own them from research through production
- Find where current methods fail on real claims, appeals and payer rules, and design approaches that hold up where they do not
- Build methods that generalise across diverse document types, payers and rule sets, and run reliably at scale
- Advance how the platform validates documentation against CMS rules, extracts structured data across documents, and generates audit-ready, fully traceable output
Technical requirements
- Proficient in Python and the modern ML stack (PyTorch or JAX, experiment tooling), with experience taking models from research to production
- Solid grounding in modern NLP and language models: training, fine-tuning and evaluation
- Experience with document understanding, information extraction, or retrieval
- Strong track record applying frontier techniques to hard, open-ended problems
Nice to have
- A track record of building with LLMs: prompt engineering, structured-output generation, and getting models to produce trustworthy output
- Experience with document AI, information extraction, or retrieval
- Publications at top conferences or journals
- Prior work in healthcare, insurance, or another regulated, document-heavy domain
Why Everi Labs
- Meaningful impact in a backed, fast-growing company
- Real-world impact: your research ships to clinics and shapes how they get paid and survive audits
- Work on the full arc, from novel method to production system used every day
- Direct influence on product direction and company trajectory as an early team member