Staff Applied Scientist Document Vision
$197,000–$197,000 year
HybridCalifornia, United States or Arizona, United States
Job Summary
Own the science of multimodal document understanding across aiR, teaching systems to read evidence the way legal professionals do from vision-language modeling to evaluation standards. Take on the hardest, most ambiguous problems in the portfolio to produce clarity: well-specified approaches, evaluations that settle questions, and systems that ship. Set standards that reach beyond your team by establishing evaluation methods, modeling patterns, and quality bars adopted by scientists you've never worked with. Multiply the team through deep review and mentorship of senior and lead scientists while advising leadership on where the science is going.
Required Qualifications
- A master's or PhD in computer science or another quantitative discipline (or equivalent professional experience)
- At least 6 years of applied AI/ML experience
- Years of production AI/ML behind you: systems you specified, shipped, and operated at scale, in partnership with the engineers who run them
- Expert judgment about AI systems: you've built them, measured them, and formed views of their limits that hold up under challenge
- Scientific rigor other people borrow: you supervise the data understanding of teams beyond your own, your evaluations become the template, and your error analyses end debates
- Software-engineering judgment trusted across teams, and the programming skill to credibly prototype what you propose
- An ownership mindset that extends across the organization
- Algorithms
- Computer Vision
- Data Analysis
- Data Science
- Deep Learning
- Machine Learning (ML)
- Natural Language
- Natural Language Processing (NLP)
- Python (Programming Language)
- Scientific Research
Desired Qualifications
- Experience with vision-language or document-understanding models (layout analysis, OCR-adjacent pipelines, table and figure extraction) in production
- An interest in legal technology and the justice system
- Experience developing information retrieval systems
- Experience developing agentic harnesses
- Experience building reliable AI systems at scale
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