opengreenhouseg2vp
Principal Product Manager - AI & Intelligent Manufacturing Systems
Fictiv
LocationOakland, CA Office
Last observed2026-06-13 05:24:47.516388
Job idg2vp-fictiv:greenhouse:8542439002
About MISUMI Americas MISUMI Americas, a division of MISUMI Group, is a leading provider of standard, configurable, and custom manufacturing solutions. By integrating a vast catalog of components with a world-class digital manufacturing platform, MISUMI Americas empowers engineers and procurement teams to accelerate innovation across the entire product lifecycle. With operations in the San Francisco Bay Area and Chicago, the company serves as a vital partner for the most innovative companies in the Americas. Impact In This Role As our Principal Product Manager - AI & Intelligent Manufacturing Systems, you’ll serve as a strategic and technical thought leader driving the vision, roadmap, and execution of AI-powered capabilities across Fictiv’s Manufacturing Quoting and Fulfillment Platform. This is a highly visible, high-leverage role — one that shapes how AI and automation become foundational to Fictiv’s operating system for new product development (NPD). You’ll partner directly with our AI R&D, data science, and platform architecture teams to define the core intelligence layer that powers quoting, supply orchestration, and manufacturing decision-making at scale. You will own and evolve the product strategy that fuses advanced AI models, data-driven automation, and manufacturing domain intelligence into a cohesive system that sets new standards for operational efficiency, precision, and speed. This role is ideal for a seasoned product leader with experience bringing AI-first enterprise platforms to life — someone equally comfortable discussing model architectures and data pipelines as they are business value and market differentiation. You will report to the VP, Product Management. Areas of Responsibility: 1. AI Technology Strategy & Product Vision Define the long-term AI technology strategy for Fictiv’s digital manufacturing platform — including where and how to leverage LLMs, machine learning, computer vision, and reinforcement learning Partner with AI research and engineering leadership to translate R&D advances into scalable, customer-facing capabilities Identify and prioritize core AI product opportunities — from intelligent quoting and auto-classification of 3D models to predictive supplier matching and adaptive routing engines Drive the architectural vision for Fictiv’s AI intelligence layer, ensuring alignment between data infrastructure, ML systems, and platform integration Evangelize the role of AI within the organization — educating teams on capabilities, limitations, and ethical deployment of intelligent systems 2. Deep Technical Product Leadership Serve as the product owner for AI infrastructure and model lifecycle management, including data acquisition, training, deployment, monitoring, and feedback loops Partner with ML engineers and data scientists to design human-in-the-loop workflows that continuously improve system accuracy and explainability Own the AI feature pipeline: define use cases, establish model performance metrics (precision, recall, latency, cost), and measure impact on business KPIs (quote accuracy, margin, lead time) Collaborate with platform engineering to ensure scalability, modularity, and compliance in AI integrations Drive responsible AI practices, including fairness, transparency, and auditability in model-driven decision systems 3. Solutions Integration & Application Development Lead the development of AI-driven application modules — such as automated quoting, manufacturability assessment, supplier recommendation, and production forecasting Work with product teams across quoting, fulfillment, and partner management to identify where intelligence adds differentiated value Ensure AI and automation capabilities are embedded seamlessly into Fictiv’s core user experiences and operational workflows Act as a bridge between AI technology development and solution commercialization, ensuring innovations move efficiently from lab to production 4. Metrics, Experimentation, and Validation Define the pe
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