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Staff Product Manager, Agentic Quoting Tools

Hybrid work

Publication date: 08/11/2026

Xometry (NASDAQ: XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry's digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity. We are hiring a Staff Product Manager to own the request-to-quote experience end to end, sitting between Sales and the quoting teams. The role sits in the Pricing and Manufacturability (P&M) group alongside the product managers for cost prediction, pricing, and Design for Manufacturability (DFM), and reports to the head of P&M product. You will be given a metric and a problem space, not a fully scoped backlog. The role requires a hybrid schedule (3 days a week) at Waltham, MA or North Bethesda, MD office location. This is a startup pace inside a public company.

Responsibilities

Own the request-to-quote experience for most important customers, mapping every quote request path from customer contact through Sales, quoting, pricing review, and back to the customer; Remove unnecessary steps; Redesign intake funnels by channel, deciding where requests are read by an agent, routed to a person, or self-served; Own the roadmap for agentic quoting, including agents that read requests and build quotes, assistants that propose margin and lead time, and automation that promotes human-priced parts into the instant-pricing engine; Define what agents do, what humans confirm, and what stays human, with defined criteria for good and failure before engineering starts; Own the integration layer across Salesforce, quoting systems, file storage, and the ERP, including Model Context Protocol (MCP) servers, tool definitions, permissions, and audit trails; Own internal quote review, pricing, and quote summary staff tools and track adoption; Instrument human pricing decisions as structured data and write own queries and dashboards; Run continuous discovery with quoters, account managers, inbound queues, and session recordings, validating with working prototypes; Define a 6 to 12 month agentic quoting roadmap and align Engineering, Data Science, Sales, Quoting Operations, and Finance; Operate spec-first, building and shipping with AI agents and prototyping in Cursor, Claude, and Figma Make before writing requirements; Share AI workflows that worked and failed with peer product managers

Requirements

8+ years of product management experience, including direct ownership of a workflow or operations product that sits between a sales or service organization and the customers it serves, in a B2B, marketplace, or enterprise context; Evidence of shipping AI or automation into an expert human workflow with guardrails, and of knowing when not to automate; Experience integrating products across systems of record (CRM, ERP, file storage, messaging), and a working understanding of how agents connect to those systems through tool protocols such as MCP; Multi-channel thinking: experience redesigning how requests enter a business (email, text, forms, APIs, agents) and showing the drop-off removed; AI-native practice, with hands-on fluency with Cursor, Claude, or equivalent agentic tools, and a point of view on evaluating large language model (LLM) features before and after launch; Strong analytical fluency: writes own SQL, has built own dashboards, and has caught a broken metric before it misled a decision; Operational curiosity: learns how a process runs by sitting with the people who run it; Exceptional written communication; Comfort making decisions under ambiguity and translating loosely defined problems into testable hypotheses and plans; Bachelor's degree in Business, Computer Science, Engineering, or a related field

Nice to have

Shipped or owned an integration between an AI agent and a system of record, with clear understanding of agent permissions, human escalation, and error detection; Shipped an AI or machine learning (ML) feature into a human-in-the-loop process with defined evaluation criteria, failure modes, and post-launch monitoring; Case where the team decided differently because of an AI-driven workflow (continuous customer signal, prototype-first decisions, evaluation criteria written before build); Experience in manufacturing, supply chain, pricing, or quoting, or demonstrated fast learning of unfamiliar operational domains

Benefits

401(k) match; Medical, dental, and vision insurance; Life and disability insurance; Generous paid time off including vacation, sick leave, floating and fixed holidays; Maternity and bonding leave; EAP and other wellbeing resources; Bonus and equity as part of total compensation
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