Principal Product Manager, Research and AI - Data

Adobe Adobe · Enterprise · San Jose, CA +2

Principal Product Manager for Adobe's Research and AI team, focusing on defining and driving the data roadmap for foundational generative AI models. This role involves sourcing and processing training data for multimodal generative capabilities, acting as a bridge between data engineering, applied science, and product strategy to scale training data and improve downstream model performance.

What you'd actually do

  1. Define and own the data product roadmap, including the end-to-end pipeline to significantly scale training data in generative models.
  2. Build systems and processes to increase value from data: prioritize what we acquire, how we process it, and ensure it maps directly to downstream model performance needs.
  3. Translate between modeling requirements and data reality – transform customer asks into concrete, actionable data programs.
  4. Partner closely with data engineering and applied science to build feedback loops that close the gap between model evaluation and data approach.
  5. Build institutional knowledge around data decisions - what was included, why, how it was processed - so the team can reason about model behavior and iterate faster.

Skills

Required

  • 7+ years of product management experience
  • Bachelor’s degree in computer science, engineering, or equivalent experience
  • Strong technical depth
  • Ability to translate between technical and non-technical collaborators
  • Detailed thinking
  • Proactivity and comfort with ambiguity
  • Attitude centered on balancing value, coverage, quality distributions, and downstream impact
  • Great partner collaboration

Nice to have

  • Experience with generative AI
  • Experience building tools for creatives and designers

What the JD emphasized

  • significant portion in AI/ML or data-intensive environments
  • Strong technical depth to hold conversations about data pipelines, model training, quality metrics, and evaluation frameworks.
  • Detailed thinking to build for scale.
  • Proactivity and comfort with ambiguity to refine problem statements and move fast without waiting for perfect information.
  • Attitude centered on balancing value, coverage, quality distributions, and downstream impact.

Other signals

  • define and drive our data roadmap
  • sourcing and processing training data
  • multimodal generative capabilities
  • data engineering, applied science, and product thinking
  • key connection between the data organization and modeling team