Senior Product Manager, Tech, Amazon Manufacturing Services

Amazon Manufacturing Services (AMS) is seeking a Sr. Product Manager, Tech to own the product vision, strategy, and roadmap for the manufacturing execution and automation intelligence stack powering Amazon's first advanced manufacturing facility - a highly automated, first-of-its-kind operation. This facility integrates industrial robotics, end-to-end manufacturing automation, and digital manufacturing to produce systems for Amazon's global fulfillment network.

Key job responsibilities

Manufacturing Execution Product Strategy

• Own the 2-3 year product roadmap for the manufacturing execution stack - from scheduling UX through operator tools to AI-powered optimization
• Define the product vision that connects AI-native scheduling intelligence to the physical shop floor experience
• Develop and maintain the execution technology strategy across P0 (manual + semi-automated), P1 (AGV integration, real-time optimization), and P2 (AI-enabled autonomous control)
• Write compelling narratives (PR/FAQs, 6-pagers, OP docs) that articulate product strategy and secure investment from leadership

Scheduling & Operator Experience

• Serve as the operational product partner to the scheduling engineering team - translating manufacturing floor needs, pain points, and workflows into product requirements
• Own the operator-facing scheduling experience: how assignments surface, how disruptions are communicated, how overrides are captured, and how the system explains its decisions to the right audience
• Drive the scheduling system's authority progression (Shadow Advisory Co-Pilot Decision Maker) by defining success criteria, measuring override rates, and building supervisor trust through UX design
• Define product requirements for scheduling and explainability tools from the operator/supervisor perspective

Machine Connectivity & IIoT

• Own the product vision for machine connectivity - defining how equipment telemetry flows from factory floor to data lake to decision systems
• Define integration requirements for manufacturing equipment onboarded at the manufacturing facility (industrial lasers, robotic welding cells, automated coating lines, autonomous mobile robots)
• Develop product requirements for digital twin capabilities - real-time equipment state, simulation for what-if analysis, and predictive modeling
• Partner with automation engineers to define the machine-to-cloud data contract for each equipment class

AI/ML & Continuous Improvement

• Define and execute product strategy for AI-driven manufacturing intelligence: predictive maintenance, automated quality inspection, process parameter optimization, and autonomous cell control
• Own requirements for the data platform layer that enables ML - feature stores, event streams, model serving infrastructure
• Drive the feedback loop between quality outcomes (first-pass yield, scrap rates) and upstream process adjustments - ensuring the system learns and improves continuously
• Define the operator interaction model for AI recommendations - when to alert, when to auto-act, when to require human confirmation

Shop Floor Quality & Compliance

• Own the product experience for in-line quality: inspection workflows, non-conformance reporting, root cause analysis tools, and SPC dashboards
• Define how quality data flows back to both the scheduling system (for replan triggers) and the enterprise system (for financial variance reporting)
• Partner with Quality Engineering to translate ISO 9001:2015 requirements into tool capabilities and audit-ready data records

A day in the life

In the morning, you review overnight production data from our prototyping factory - the scheduling system's override rate dropped to 7% this week, and you're preparing the case to promote it from Shadow to Advisory mode. You pull the override-reason breakdown to identify two UX issues driving unnecessary supervisor interventions, then draft requirements for the engineering team.

Mid-morning, you join the scaled factory equipment onboarding review. The laser cutting integration team needs a decision on telemetry frequency - you work through the tradeoffs between data granularity (better for predictive maintenance models) and network cost (lower in batch mode), landing on a tiered approach by signal type.

After lunch, you're on the prototype factory floor shadowing a powder coating operator through a disruption scenario - a rush order just reshuffled the queue, and you observe how the shop floor app communicates the change. The operator missed the notification; you sketch a design change in your notebook.

You close the day reviewing the ECO blast-radius prototype with the engineering team. An engineering change landed that affects 47 in-flight orders - the impact visualization needs to surface at-risk-dollars more prominently for the Change Control Board's decision meeting tomorrow.