Back to search
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. Basic Qualifications: - Bachelor's degree or above in Computer Science, Engineering, or related fields
- 7+ years of product or program management, product marketing, business development or technology experience
- Experience defining roadmap strategy and prioritizing deliverables for your team products
- Experience contributing to engineering discussions around technology decisions and strategy related to a product
- Experience with analytical tools and ability to dive deep into metrics and reporting
- Experience with manufacturing execution systems (MES), shop floor scheduling, or production control software
- Experience managing technical products or online services in manufacturing or industrial environments Preferred Qualifications: - Experience in high-volume manufacturing operations or sourcing environments
- Experience in practical work applying ML to solve complex problems
- Experience with concepts such as system architecture, optimization, system dynamics, system analysis, statistical analysis, reliability analysis, and decision making
- Experience in building and driving adoption of new tools
- Knowledge of cutting-edge production technologies and delivery workflows
- Master's degree in Computer Science, Computer Engineering, Systems Engineering, Electrical Engineering, or other related discipline
- Experience presenting complex ideas in writing in the form of authoring white papers, proposals, or other formal strategy documents
- Knowledge of IIoT protocols (OPC UA, MTConnect, MQTT) and industrial data integration
- Experience in high-volume, high-mix manufacturing environments (metals fabrication, welding, coating, assembly)
- Experience with ISO 9001 or similar manufacturing quality management systems
- Knowledge of Industry 4.0 principles, smart manufacturing architectures, or software-defined manufacturing
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Bellevue - 151,200.00 - 204,600.00 USD annually
Learn more about this Employer on their Career Site
