Note: The job is a remote job and is open to candidates in USA. Relativity is a data-centered, AI-native legal technology company, and they are seeking a Senior Manager, Applied Science to lead a team expanding the aiR agentic harness for greater capability and reliability. The role involves leading a team of applied scientists, setting technical standards, and ensuring the readiness of AI systems while collaborating with various stakeholders to bring ideas to production.
Responsibilities
- Lead and grow a team of applied scientists: hire, coach, set direction, and develop people toward their best work
- Set the technical and scientific bar. The work stays hands-on: you'll shape architectures, review designs and evaluations, and dig into hard problems alongside your team, close enough to the science to lead by example
- Own AI system readiness end-to-end, from problem framing through evaluation, error analysis, efficacy studies, and production monitoring, so that what ships is dependable and defensible
- Choose the right problems. Current examples range from agentic assistants that extend what a legal professional can do, to large-scale review and analysis that must stay reliable across hundreds of thousands of documents per matter. You'll help decide where we invest
- Partner with product, engineering, design, customer-facing teams, and the legal experts on the team to take ideas from proof-of-concept to production at scale
- Communicate with precision to your team, to leadership, and to customers: translate technical nuance into decisions people can act on, and carry the customer's voice back into the work
- Represent Relativity at industry conferences, events, and with customers
Skills
- A master's or PhD in computer science or another quantitative discipline (or equivalent professional experience), and at least 6 years in applied AI/ML, including at least 1 year as a people leader
- Deep applied AI/ML and deployment engineering experience: you've built production-ready AI systems and owned them through their production lifecycle, partnering with engineering teams to keep them running reliably
- Fluency with modern generative AI as a component of larger systems, and sound judgment about what it can and cannot do reliably
- Machine-learning rigor, grounded in data understanding: careful evaluation, error analysis, and the statistical thinking to draw only the conclusions your data supports
- Strong software-engineering judgment and programming skill
- An ownership mindset that extends beyond your immediate team
- Artificial Intelligence (AI)
- Business Intelligence (BI)
- Data Analysis
- Database Management
- Data Governance
- Data Intelligence
- Data Visualization
- Information Management
- Machine Learning (ML)
- Strategic Planning
- 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
Benefits
- Remote/Hybrid
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