1389 Ai Platform Principal Program Manager jobs in Seattle,WA

Bachelor's degree from an accredited college in a related discipline, or equivalent experience/combined education
Demonstrated critical thinking, analytical, problem-solving, and communication skills
Well organized and detail-oriented
Intermediate proficiency with Microsoft Office applications
2-4 + years of experience in inventory control, material planning, or production scheduling
Demonstrated success performing in a team environment
Ability to obtain and maintain a DoD Secret clearance with an Interim Secret clearance obtained prior to start; U.S. citizenship is required
Security Clearance Statement: This position requires a government security clearance, you must be a US Citizen for consideration
Clearance Level: Secret
Ability to work onsite at a Lockheed Martin facility (Onsite Full-time)

Principal Technical Product Manager, Liquid Cooling, Mechanical Products and Services

Amazon Data Services

Seattle, WA 98127

Bachelor's degree
7+ years of technical product or program management experience
5+ years developing and validating hardware products for direct-to-chip or immersion forms of liquid cooling
5+ years defining liquid cooling product strategies aligned to global data center infrastructure scale and AI hardware needs

Reliable Robotics

Seattle, WA 98127

Disability Solutions

Seattle, WA 98127

RAFT

Tacoma, WA 98417

Artech Information Systems

Lynnwood, WA 98036

Amazon.com LLC

Seattle, WA 98127

8+ years of technical product or program management experience
Experience with feature delivery and tradeoffs of a product
6+ years of end to end product delivery experience
Experience owning/driving roadmap strategy and definition
Experience leading engineering discussions around technology decisions and strategy related to a product
Bachelor's degree
Experience managing and deploying ML products
Experience leading and influencing your team or organization, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
Background in an applied science, engineering, or quantitative field, with a solid understanding of the model lifecycle, data readiness, and model evaluation frameworks.