AI Product Manager

Scale AI Scale AI · Data AI · New York, NY +1 · Gen AI Sales

AI Product Manager to own the Agent & Reinforcement Learning Environments data vertical, focusing on Computer Using Agent (CUA) data. Responsibilities include owning the product roadmap, data generation pipelines, quality, and researcher-facing tools for training and evaluating intelligent agents. Requires a blend of entrepreneurial, go-to-market, and technical skills, with experience in product management and understanding of RL, simulation environments, and data pipelines for model training/evaluation.

What you'd actually do

  1. Own the roadmap for the Agent & RL Environment Data vertical, setting product direction and driving execution across engineering, operations, and go-to-market teams.
  2. Build technical partnerships with research teams at leading AI labs, identifying insights that shape new product lines and competitive strategies for your vertical.
  3. Design, experiment with, and deliver high-quality data pipelines, tooling, and evaluation frameworks that advance RL and agentic model capabilities.
  4. Scope out and scale the creation of RL environments that simulate real-world use cases.
  5. Collaborate cross-functionally, influencing business priorities and diving in the weeds of research, operations, and customer interactions.

Skills

Required

  • 6+ years of experience in product management or a customer-facing role
  • Software engineering background (a degree in computer science or equivalent experience)
  • Understanding of reinforcement learnings, simulation environments, or data pipelines for model training and evaluation
  • Strong customer intuition and the ability to translate technical requirements into impactful product decisions
  • Bias for action and comfort wearing multiple hats and operating in fast-moving environments

Nice to have

  • Entrepreneurial mindset: A builder excited by ambiguity and motivated to create new products from the ground up.

What the JD emphasized

  • Agent & Reinforcement Learning Environments
  • Computer Using Agent (CUA) data
  • data as a product
  • pipelines for data generation and quality
  • researcher-facing tools
  • train and evaluate intelligent agents
  • technical expert
  • AI research
  • go-to-market mindset
  • technical depth
  • product roadmap
  • engineering, operations, and go-to-market teams
  • technical partnerships with research teams
  • new product lines
  • competitive strategies
  • data pipelines, tooling, and evaluation frameworks
  • RL and agentic model capabilities
  • RL environments
  • real-world use cases
  • cross-functionally
  • business priorities
  • research, operations, and customer interactions
  • Software engineering background
  • reinforcement learnings
  • simulation environments
  • data pipelines for model training and evaluation
  • customer intuition
  • technical requirements into impactful product decisions
  • Bias for action
  • fast-moving environments

Other signals

  • AI data foundry
  • Agent & Reinforcement Learning Environments
  • Computer Using Agent (CUA) data
  • data as a product
  • pipelines for data generation and quality
  • researcher-facing tools
  • train and evaluate intelligent agents
  • technical expert
  • AI research
  • go-to-market mindset
  • technical depth
  • product roadmap
  • engineering, operations, and go-to-market teams
  • technical partnerships with research teams
  • new product lines
  • competitive strategies
  • data pipelines, tooling, and evaluation frameworks
  • RL and agentic model capabilities
  • RL environments
  • real-world use cases
  • cross-functionally
  • business priorities
  • research, operations, and customer interactions
  • Software engineering background
  • reinforcement learnings
  • simulation environments
  • data pipelines for model training and evaluation
  • customer intuition
  • technical requirements into impactful product decisions
  • Bias for action
  • fast-moving environments
  • AI data foundry
  • frontier model training
  • enterprise adoption
  • defense applications
  • autonomous vehicles
  • reliable AI systems
  • high-quality data
  • full-stack technologies
  • AI applications
  • industry leaders
  • Meta
  • Cisco
  • DLA Piper
  • Mayo Clinic
  • Time Inc.
  • Government of Qatar
  • U.S. government agencies
  • Army
  • Air Force
  • AI applications