Product Manager, Technical

T-Mobile T-Mobile · Telecom · Bellevue, WA +1

Product Manager, Technical role focused on T-Mobile's Physical & Edge AI business, leading the product lifecycle for physical-world AI offerings including intelligent IoT, sensors, and computer vision. The role involves defining use cases, prioritizing platform features, and ensuring technical delivery in collaboration with various partners, with success measured by product performance, adoption, and revenue.

What you'd actually do

  1. Lead the end-to-end product lifecycle by defining vision, strategy, and roadmap for Physical & Edge AI use cases and reference solutions, aligned with business and technical goals
  2. Conduct market and customer research and run technical discovery with hardware/OEM and ISV partners to identify opportunities and inform product decisions
  3. Translate product strategy into detailed features and user stories, handling the product backlog to support Agile development teams
  4. Partner with Solutions Architects to validate the architecture, performance, and security of edge deployments end-to-end
  5. Monitor product performance and quality in production (e.g., deployment time, edge latency/accuracy, adoption, revenue), addressing defects and driving continuous improvement

Skills

Required

  • Agile SDLC
  • Analytical Thinking
  • Backlog Management
  • Customer-Focused
  • Product Management
  • Product Requirements
  • Product Specifications
  • Solution Architecture Design
  • Stakeholder Management
  • Technical Design Documentation
  • Computer Science
  • Engineering
  • IT
  • technical product management
  • Agile software product development

Nice to have

  • Edge, IoT, or computer-vision products
  • Edge inference and sensor/camera protocols (e.g., MQTT, ONVIF, RTSP)
  • Physical-space verticals: retail, manufacturing, logistics, public sector, or healthcare
  • Certified Scrum Master (CSM) or Scrum Product Owner (CSPO)
  • Agile Certified Practitioner (PMI-ACP)

What the JD emphasized

  • physical-world AI
  • computer vision for physical spaces
  • edge deployments
  • edge latency/accuracy

Other signals

  • physical-world AI
  • intelligent IoT
  • computer vision for physical spaces
  • edge deployments
  • edge latency/accuracy