Head of Applied AI for Legal Tech

Relativity; Detroit (MI); Hybride; USD 208 000 - 312 000; Plein temps

Résumé du poste

Relativity is seeking a leadershi p role in Applied Science to guide development of production-ready AI systems for legal tech. You will shape architectures, mentor researchers, and ensure rigorous evaluation across large document analyses.

You will own end-to-end readiness, decide priorities, and collaborate with product, engineering, design, and legal teams to deliver reliable, scalable AI solutions at scale.

Qualifications

• Master's or PhD in computer science or a quantitative field, or equivalent experience.
• At least 6 years in applied AI/ML with leadership experience.
• Experience deploying production AI systems and lifecycle ownership.
• Fluency with modern generative AI and sound judgment of its limits.
• Strong statistical thinking and data-driven evaluation.

Responsabilités

• Lead and grow a team of applied scientists, hiring and coaching.
• Shape architectures, review designs, and work hands-on with the team.
• Own AI system readiness end-to-end from framing to production monitoring.
• Decide which problems to tackle and align with product and customers.
• Collaborate with cross-functional teams to take ideas to production at scale.
• Communicate technical nuance clearly to leadership and customers.
• Represent Relativity at industry events and with customers.

Connaissances

Applied AI/ML

Production-ready AI

Software engineering

Team leadership

Data understanding

Generative AI

Formation

Master's or PhD in computer science or quantitative discipline

Description du poste

Relativity is seeking a leadershi p role in Applied Science to guide development of production-ready AI systems for legal tech. You will shape architectures, mentor researchers, and ensure rigorous evaluation across large document analyses.

You will own end-to-end readiness, decide priorities, and collaborate with product, engineering, design, and legal teams to deliver reliable, scalable AI solutions at scale.