Senior Product Manager, Tech, Manufacturing Services

Position: Senior Product Manager, Tech, Amazon Manufacturing Services
Description

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…