Staff Applied Scientist, Multimodal Document Vision for Legal

Relativity; Kentucky; Hybrid; USD 197,000 - 295,000; Full time

Job summary

Relativity is seeking a Staff Applied Scientist to tackle the hardest problems in document vision within aiR. You will own multimodal document understanding, set standards, mentor scientists, and influence product direction.

The role blends research excellence with production readiness, collaborating with engineering and Applied Science leadership to scale reliable AI systems across vast legal data.

Qualifications

• Requires a master's or PhD in computer science or a quantitative field with strong AI/ML foundations.
• Minimum 6 years of applied AI/ML experience in production environments.
• Proven ability to design, ship, and operate AI systems at scale with strong engineering collaboration.

Responsibilities

• Take on the hardest, most ambiguous problems in the portfolio and produce clarity: a well-specified approach, an evaluation that settles the question, a system that ships.
• Set standards that reach beyond your team: evaluation methods, modeling patterns, and quality bars adopted by scientists you've never worked with.
• Own readiness for flagship AI systems, from problem framing through efficacy studies and production monitoring, in partnership with engineering.
• Multiply the team through deep review and mentorship of senior and lead scientists.
• Advise Applied Science leadership on where the science is going and where we should invest.

Skills

Algorithms

Computer Vision

Data Analysis

Data Science

Deep Learning

Machine Learning

Natural Language

Natural Language Processing

Python

Scientific Research

Education

Master's or PhD in CS or quantitative field

Job description

Relativity is seeking a Staff Applied Scientist to tackle the hardest problems in document vision within aiR. You will own multimodal document understanding, set standards, mentor scientists, and influence product direction.

The role blends research excellence with production readiness, collaborating with engineering and Applied Science leadership to scale reliable AI systems across vast legal data.