Product Manager, Data Engine

Scale AI Scale AI · Data AI · Washington, DC · Public Sector

Product Manager for Scale AI's Public Sector Data Engine, focusing on building ML Ops infrastructure for computer vision and generative AI models used in national security systems. The role involves architecting the AI engine, managing roadmaps, technical scoping, and operationalizing collaboration between engineering, operations, and government stakeholders.

What you'd actually do

  1. Architect the AI Engine: Drive the roadmap for Public Sector ML Ops tools, ensuring they serve as the "ground truth" foundation for building, evaluating, and deploying AI systems.
  2. Bridge Custom & Scale: Support diverse customer profiles—from massive-scale satellite and video labeling engagements to complex, bespoke ML Ops partnerships.
  3. Own Technical Scoping: Partner with Engineering to make high-stakes design decisions on infrastructure, APIs, and model evaluation frameworks (T&E).
  4. Operationalize Collaboration: Use high EQ and structured thinking to align Engineering, Operations, and elite Government stakeholders.

Skills

Required

  • Background in Software Engineering, Field Engineering, or Vision Systems
  • Experience in Computer Vision or ML Ops
  • Understanding of the model lifecycle from data acquisition to Test & Evaluation
  • Ability to read code and understand system architecture
  • Structured thinking and action orientation

Nice to have

  • Active Secret or TS/SCI clearance
  • Advanced degree (CS, Engineering, or related field) or equivalent experience
  • Practical experience designing, building, or evaluating large-scale Computer Vision models
  • Advanced data processing and deployment techniques

What the JD emphasized

  • technical, high-horsepower
  • ML Ops infrastructure
  • computer vision
  • generative AI
  • foundational engine
  • model evaluation
  • Test & Evaluation (T&E)
  • Computer Vision
  • ML Ops
  • lifecycle of a model
  • data acquisition
  • Test & Evaluation

Other signals

  • ML Ops infrastructure
  • computer vision
  • generative AI
  • model evaluation
  • data management
  • curation
  • model development