Staff Product Manager, Agentic Quoting Tools

Xometry•North Bethesda, MD

3d•$180,000 - $207,000•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, alongside product managers for cost prediction, pricing, and Design for Manufacturability. It reports to the head of P&M product and requires a hybrid schedule, three days a week, at the Waltham, MA or North Bethesda, MD office. The role focuses on improving how quote requests arrive, are processed, priced, reviewed, and returned to customers. The product manager will shape agentic quoting tools, integrations that connect agents and people with quoting systems and the ERP, and internal tools used by quoting and sales teams. Success is measured by quote turnaround time, cost per quote, quote accuracy, quote-to-order conversion on the human-assisted path, and adoption of staff tools. This is a high-ambiguity role with responsibility for defining a 6–12 month roadmap, conducting continuous discovery, building and testing prototypes, setting evaluation criteria and guardrails for AI features, instrumenting workflows, and aligning Engineering, Data Science, Sales, Quoting Operations, and Finance. The role calls for an AI-native, hands-on, spec-first product practice and a startup pace within a public company.

Requirements

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 of knowing 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 and demonstrating removed drop-offs.
AI-native practice, including examples where AI changed a team decision, not only saved time.
Hands-on fluency with Cursor, Claude, or equivalent agentic tools.
A point of view on evaluating LLM features before and after launch.
Strong analytical fluency, including writing SQL, building dashboards, and identifying broken metrics.
Operational curiosity and 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.
Ability to make decisions under ambiguity and turn loosely defined problems into testable hypotheses and plans.
Bachelor's degree in Business, Computer Science, Engineering, or a related field.
Ability to work at a startup pace, moving quickly from hypothesis to shipped test.

Nice To Haves

Experience in manufacturing, supply chain, pricing, or quoting.
Ability to demonstrate learning an unfamiliar operational domain quickly.
Experience redesigning a request-to-fulfillment funnel across sales, service, and customers, with channels, process changes, before-and-after metrics, and adoption results.
Experience building or owning an AI-agent integration with a system of record and explaining permissions, human approval requirements, and error detection.
Experience shipping an AI or ML feature into a human-in-the-loop process and describing evaluation criteria, failure modes, and post-launch monitoring.
Experience with a case where an AI-driven workflow changed a team decision through continuous customer signal, prototype-first decisions, or evaluation criteria written before build.

Responsibilities

Own the end-to-end request-to-quote experience for important customers and map the process from intake through customer response.
Improve quote turnaround time, cost per quote, quote accuracy, and quote-to-order conversion on the human-assisted path.
Redesign intake pathways across channels, including email, text, uploads, forms, APIs, and agents; identify drop-offs and determine where work should be automated, routed to staff, or self-served.
Own the roadmap for agentic quoting, including agents that interpret requests and files, assistants that propose margin and lead time, and automation that promotes human-priced parts into instant pricing.
Define success criteria, guardrails, and failure modes for AI features before engineering begins, and monitor them through and after launch.
Own the integration layer connecting agents and people across Salesforce, quoting systems, file storage, and the ERP, including MCP servers, tool definitions, permissions, and audit trails.
Build and own internal quote review, pricing, and quote summary tools; measure success through adoption by quoting and sales teams.
Structure human pricing decisions, such as overrides, margin changes, and lead-time adjustments, as data to improve predictions and future agent runs.
Write queries, build dashboards, and specify required product events and instrumentation.
Conduct continuous discovery with quoters, account managers, customer communications, session recordings, and live customer and staff conversations; use and validate AI-assisted synthesis.
Create working prototypes and test them with intended users to inform build decisions.
Define and sequence a 6–12 month agentic quoting roadmap based on customer impact and speed, and align Engineering, Data Science, Sales, Quoting Operations, and Finance.
Present progress against named outcomes to technical and non-technical audiences.
Use a spec-first workflow: inspect relevant systems with agentic tools, write implementation-ready specifications, and prototype in Cursor, Claude, and Figma Make before writing requirements.
Share successful and unsuccessful AI workflows and patterns with other product managers.
Move quickly from hypotheses to shipped tests in an ambiguous problem space.