Product Manager, ML Operations

Posting Type

Hybrid

Job Overview

At Relativity we make software to help users organize data, discover the truth, and act on it. Our e-discovery platform is used by more than 13,000 organizations around the world to manage large volumes of data and quickly identify key documents during litigation, internal investigations, and compliance projects. 

We are looking for a Product Manager to own Relativity’s AI platform, our internal set of tooling and technology that manages the AI lifecycle for internal teams. These teams rely on our AI Platform to access, build, test, deploy, and monitor AI models and products. You will collaborate closely with applied scientists, engineers, and product managers to understand their workflows and pain points related to AI product development and delivery. Ruthless prioritization based on data is your mantra.

A successful candidate will have a strong technical background as well as experience taking AI/ML products from experimentation to production. We are looking for someone who is energized by helping teams be more efficient in building and deploying AI products. 

Job Description and Requirements

Role Responsibilities  

 

Accelerate AI Innovation: 

• Faster time to value. Own the success of initiatives around reducing applied science experimentation cycle time and reducing the time it takes our AI teams to validate their products both in the lab and in production. 

• Create self-service capabilities. Empower engineering and applied scientist teams to leverage our AI platform as easily as possible. 

• Streamline AI/ML lifecycle. Identify and resolve bottlenecks in our AI/ML lifecycle across each of our AI life cycle testing environments. 

 

Product Ownership & Strategy  

• Customer Centric. Run continuous discovery sessions with consumers of our AI platform (engineers, product managers, applied science) to uncover and test product opportunities.  

• Define Success. Partner with Engineering and internal platform consumers to define quarterly OKRs (Objectives and Key Results) that articulate team goals. 

• Measure success. Define and track success metrics to measure platform adoption, efficiency gains, and business impact. 

 

Technical Expertise  

• Partner with engineering: Partner with your engineering team to ensure deliverables meet our high standards for scalability, reliability, observability, security, and AI ethics.  

• Lead tradeoff decision-making: Through a strong understanding of AI/ML workflows, technologies, and data pipelines lead product tradeoff and scope discussions with engineering, other product managers, and applied science.  

• Simplify complex topics: Translate technical conversations and decisions to less technical audiences, internal and external, to create clarity with stakeholders. 

 

Team Collaboration  

• Share context: Clearly articulate the “why” behind product decisions to foster alignment and buy-in from internal and external stakeholders.  

• Lead cross-functional initiative