Senior Manager, Applied Science
$208,000–$208,000 year
HybridCalifornia, United States or Arizona, United States
Job Summary
Lead and grow a team of applied scientists by hiring, coaching, and setting technical direction while shaping architectures and reviewing designs alongside the science. Own AI system readiness end-to-end, from problem framing through evaluation and production monitoring to ensure outputs are dependable and defensible. Choose high-impact problems ranging from agentic assistants to large-scale document review, partnering with product, engineering, and legal experts to move ideas from proof-of-concept to production at scale. Communicate with precision to translate technical nuance for leadership and customers, and represent Relativity at industry conferences. This role supports Relativity's mission to organize data and discover truth within the legal tech industry.
Required Qualifications
- Master's or PhD in computer science or another quantitative discipline (or equivalent professional experience)
- At least 6 years in applied AI/ML
- At least 1 year as a people leader
- Deep applied AI/ML and deployment engineering experience
- Experience building production-ready AI systems and owning them through their production lifecycle
- Fluency with modern generative AI as a component of larger systems
- Sound judgment about what generative AI can and cannot do reliably
- Machine-learning rigor grounded in data understanding
- Careful evaluation, error analysis, and statistical thinking
- Strong software-engineering judgment and programming skill
- An ownership mindset that extends beyond your immediate team
- Algorithms
- Data Science
- Natural Language
- Predictive Analytics
- Project Management
- Reinforcement Learning
- Research Development
- Science
- Statistical Models
- Team Leadership
Desired Qualifications
- An interest in legal technology and the justice system
- Experience hiring and growing a team
- Experience developing information retrieval systems or agentic harnesses
- An interest in building reliable AI systems at scale
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