Staff Product Manager, Agentic Quoting Tools

Xometry•North Bethesda, MD

2d•Hybrid

About The Position

Xometry is hiring a Staff Product Manager to own the end-to-end request-to-quote experience for its most important customers. The role sits between Sales and quoting teams in the Pricing and Manufacturability (P&M) group and reports to the head of P&M product. The product manager will define a roadmap from an assigned metric and ambiguous problem space, improving quote speed, cost, accuracy, and quote-to-order conversion on the human-assisted path. The role leads the design of agentic quoting tools, including agents that read requests and files, build quotes, propose margin and lead time, and help promote human-priced parts into the instant-pricing engine. It also owns the MCP integration layer connecting agents and staff across Salesforce, quoting systems, file storage, and the ERP, as well as internal quote review, pricing, and quote summary tools. The product manager will define appropriate human oversight and AI evaluation criteria, instrument workflows and human pricing decisions, and use the resulting data to improve predictions and agent performance. The position requires continuous discovery with quoters, account managers, customers, and other staff; hands-on prototyping and use of agentic AI tools; analytical work including SQL and dashboards; and cross-functional alignment with Engineering, Data Science, Sales, Quoting Operations, and Finance. The role requires a hybrid schedule of three days per week at the Waltham, Massachusetts, or North Bethesda, Maryland, office. The estimated base salary range is $180,000–$207,000 annually, plus bonus, depending on job-related skills, relevant experience, and location.

Requirements

At least 8 years of product management experience.
Direct ownership of a workflow or operations product between a sales or service organization and its customers, in a B2B, marketplace, or enterprise context.
Evidence of shipping AI or automation into an expert human workflow with guardrails, and judgment about when not to automate.
Experience integrating products across systems of record such as CRM, ERP, file storage, and messaging systems.
Working understanding of how agents connect to systems through tool protocols such as MCP.
Experience redesigning multi-channel request intake, such as email, text, forms, APIs, or agents, and demonstrating reduced drop-off.
AI-native practice, including examples where AI changed a team decision rather than only saving time.
Hands-on fluency with Cursor, Claude, or equivalent agentic tools, and a perspective on evaluating LLM features before and after launch.
Strong analytical skills, including writing SQL, building dashboards, and identifying broken metrics.
Operational curiosity and the ability to learn processes by working with the people who run them and earning their trust.
Exceptional written communication that enables operations leads, engineers, and executives to act on the same information.
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 Haves

Experience in manufacturing, supply chain, pricing, or quoting, or demonstrated ability to learn an unfamiliar operational domain quickly.
Ability to describe a redesigned request-to-fulfillment funnel across sales, service, and customers, including channels, removed steps, before-and-after metrics, and adoption.
Experience building or owning an AI-agent integration with a system of record and explaining permissions, human approvals, and error detection.
Experience shipping an AI or ML feature in a human-in-the-loop process, including evaluation criteria, failure modes, and post-launch monitoring.
Examples of a team making a different decision because of an AI-driven workflow, including continuous customer signal, prototype-first decisions, or evaluation criteria defined before development.

Responsibilities

Own and improve the end-to-end request-to-quote experience for Xometry’s most important customers.
Map quote-request paths across email, text, uploads, agents, Sales, quoting, pricing review, and customer delivery; identify and remove unnecessary steps.
Improve quote turnaround time, cost per quote, quote accuracy, and quote-to-order conversion on the human-assisted path.
Redesign intake pathways, addressing channel-specific drop-offs and deciding when requests should be handled by agents, routed to staff, or made available for customer self-service.
Own the roadmap for agentic quoting, including agents that read requests and files and build quotes, assistants that propose margin and lead time, and automation that promotes human-priced parts into the instant-pricing engine.
Define what AI agents may do, what requires human confirmation, and what must remain a human task; define success and failure criteria for AI features before engineering begins and monitor them after launch.
Own integrations enabling agents and people to exchange work across Salesforce, quoting systems, file storage, and the ERP, including MCP servers, tool definitions, permissions, and audit trails.
Own internal quote review, pricing, and quote summary tools, and measure whether the teams adopt and use them.
Instrument human pricing decisions such as overrides, margin changes, and lead-time adjustments as structured data to improve predictions and future agent runs.
Write SQL queries, build dashboards, and specify necessary product events and instrumentation.
Conduct continuous discovery by working with quoters and account managers, reviewing inbound email and text queues, observing session recordings, and validating synthesized insights through customer and staff conversations.
Build and test working prototypes with intended users as a default input to build decisions.
Define a six-to-twelve-month agentic quoting roadmap, sequence it by customer impact and speed, and align Engineering, Data Science, Sales, Quoting Operations, and Finance.
Present progress against defined outcomes to technical and non-technical audiences.
Work spec-first, use agentic tools to understand relevant systems and assess feasibility, and write specifications suitable for engineering and coding agents.
Prototype using Cursor, Claude, and Figma Make before writing requirements.
Share successful and unsuccessful AI workflows and establish reusable patterns for other product managers.