Senior Technical Program Manager, GO-AI Robotics Platform

Amazon

North Reading (MA)

On-site

USD 148,700 - 201,200

Full time

14 days+

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Job summary

Amazon Robotics GO-AI Technology & Development Team seeks a Senior Technical Program Manager to lead cross-functional programs spanning AI automation from concept to deployment at scale. You will partner with product, science, software, data, and operations teams to accelerate evaluation, deployment, and scaling of AI solutions across Amazon Robotics.

You will design human-in-the-loop workflows, define success metrics, drive platform roadmaps, and communicate strategy to senior leadership while

Qualifications

6+ years of technical product or program management experience.
5+ years of project management disciplines including scope, schedule, budget, quality, along with risk and critical path management experience.
Experience defining technical requirements and partnering with engineering teams to deliver scalable technical solutions.
Demonstrated ability to drive clarity and define program structure in ambiguous domains.
Excellent verbal and written communication skills.

Responsibilities

Partner with other Amazon teams to understand AI automation-based use cases, evaluate partnership opportunities, and define GO-AI's technical and operational plans to support them.
Build trusted partnerships with business and technical teams, serving as the primary GO-AI partner throughout the program lifecycle.
Design and lead human-in-the-loop solutions and AI workflows that bridge science, engineering, and operations.
Partner with GO-AI Software, Science, Data, and BI Engineering teams to identify capability gaps, prioritize features, and influence long-term platform roadmaps.
Define success metrics, monitor program health, and use data to drive decisions, identify risks, and evaluate trade-offs.
Drive senior leadership reviews through clear written and verbal communication of program strategy, risks, trade-offs, and recommendations.
Drive process simplification by eliminating inefficiencies and building scalable mechanisms.