Senior Product Manager, Tech, Amazon Manufacturing Services

Roboticsconnected; Bellevue (WA); Presencial; USD 170.000 - 210.000; Jornada completa

Descripción de la vacante

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.

This role focuses on connecting AI-native scheduling intelligence to the physical shop floor experience, defining the machine connectivity strategy, and driving continuous improvement across AI-enabled processes and quality systems.

Responsabilidades

• Manufacturing Execution Product Strategy: Own the 2–3 year roadmap for the manufacturing execution stack, from scheduling UX to operator tools and AI-powered optimization.
• Scheduling & Operator Experience: Lead scheduling engine requirements, operator-facing UX, and the progression of authority from Shadow to Decision Maker with measurable success criteria.
• Machine Connectivity & IIoT: Define telemetry flow from factory floor to data lake and decision systems; establish integration for lasers, welding cells, coating lines, and autonomous robots.
• AI/ML & Continuous Improvement: Define strategy for AI-driven manufacturing intelligence, data platform needs for ML, and feedback loops to improve outcomes.
• Shop Floor Quality & Compliance: Own in-line quality workflows, non-conformance reporting, and ISO 9001:2015 alignment for audit-ready data.

Descripción del empleo

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 trade‑offs 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.