Staff Applied Scientist — Multimodal Document Vision

Relativity; Louisville (KY); Hybrid; USD 197,000 - 295,000; Full time

Job summary

Relativity is seeking a Staff Applied Scientist to tackle its most challenging document-vision problems. You will define evaluation standards, supervise rigorous experiments, and architect scalable AI systems with a bias for reliability and defensibility.

You’ll mentor senior scientists and collaborate with engineering to ensure production readiness. This role sits in Relativity Applied Science, offering a competitive total compensation package in a data-driven, fast-paced environment.

Qualifications

• Master's or PhD in computer science or quantitative field, or equivalent experience.
• Production AI/ML experience with systems shipped and operated at scale.
• Expert judgment about AI systems, with proven limits and validation.
• Scientific rigor, supervising data understanding and evaluations.
• Software engineering judgment and credible prototyping ability.

Responsibilities

• Take on the hardest, most ambiguous problems and produce a clear plan and a shippable system.
• Set evaluation methods, modeling patterns, and quality bars across teams.
• Own readiness for flagship AI systems from framing to production monitoring.
• Mentor senior and lead scientists to multiply impact.
• Advise leadership on science directions and investment priorities.

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 Computer Science or quantitative discipline

Job description

Relativity is seeking a Staff Applied Scientist to tackle its most challenging document-vision problems. You will define evaluation standards, supervise rigorous experiments, and architect scalable AI systems with a bias for reliability and defensibility.

You’ll mentor senior scientists and collaborate with engineering to ensure production readiness. This role sits in Relativity Applied Science, offering a competitive total compensation package in a data-driven, fast-paced environment.