Staff Product Manager, Agentic Quoting Tools

Description

The company is a digital marketplace that connects people with product ideas to manufacturers who can build them, and gives manufacturers resources to grow while letting buyers at Fortune 1000 companies access global manufacturing capacity. The company is listed on NASDAQ under XMTR.

This Staff Product Manager owns the request-to-quote experience end to end, working between Sales and the quoting teams and focusing on the company's most important customers. The job is to rethink how a request becomes a priced quote across every intake channel, every handoff between people and systems, and every step that does not need a person. That covers agents that read requests and build and price quotes, Model Context Protocol (MCP) integrations that connect those agents to quoting systems and the Enterprise Resource Planning (ERP) platform, and internal staff tools for reviewing agent work and deciding what the agents should not do. Success is measured by how fast, how accurately, and with how little effort top customers get a quote they can act on.

The role sits in the Pricing and Manufacturability (P&M) group with the product managers for cost prediction, pricing, and Design for Manufacturability (DFM), and reports to the head of P&M product.

You will receive a metric and a problem space rather than a finished backlog, so you must be able to set your own roadmap from unclear signals.

The team is a startup inside a public company, and the pace is fast: you move quickly from hypothesis to a shipped test. A role that needs a clear brief and a stable backlog is not a good match.

Responsibilities

• Map every path a quote request takes today, from a customer's email, text, upload, or agent request through Sales, quoting, pricing review, and back to the customer. Learn the back-office steps well enough to explain them better than the teams running them, and remove the ones that are unnecessary.
• Measure success by quote turnaround time, cost per quote, quote accuracy, and quote-to-order conversion on the human-assisted path.
• Treat each intake channel as its own pathway with its own drop-offs and fixes. Decide where an agent reads a request, where a person takes over, and where a customer can self-serve. Make sure requests arrive structured, complete, and partly priced before a person sees them.
• Own the agentic quoting roadmap: agents that read requests and files and build quotes, assistants that suggest margin and lead time, and automation that moves human-priced parts into the instant-pricing engine over time. Decide what the agent does, what a human confirms, and what remains manual. Set the criteria for success and failure for each AI feature before engineering begins, and keep enforcing them through launch and afterward.
• Own the integration layer connecting agents and people across Salesforce, quoting systems, file storage, and the ERP. This includes MCP servers, tool definitions, permissions, and an audit trail showing who or what changed a quote and why. Design the human-agent exchange as a product.
• Own the internal tools for quote review, pricing, and quote summaries. Treat adoption as the key metric, since a tool people avoid has failed no matter what shipped.
• Make every human pricing decision, including overrides, margin changes, and lead-time adjustments, into structured data that improves future predictions and agent runs. Write your own queries and dashboards, and specify the events you need in requirements documents rather than asking analysts.
• Run discovery continuously. Work alongside quoters and account managers, read inbound email and text queues, review session recordings, and use AI tools to synthesize findings. Check findings against live customer and staff conversations before acting. Default to a working prototype tested with actual users before committing to a build.
• Build a 6 to 12 month agentic quoting roadmap, sequence it by customer impact and speed, and align Engineering, Data Science, Sales, Quoting Operations, and Finance. Report progress against named outcomes to both technical and non-technical audiences.
• Work spec-first with AI agents. Use agentic tools to read the relevant systems, ask informed feasibility questions, and write specs an engineer can give to a coding agent. Prototype in Cursor, Claude, and Figma Make before writing requirements.
• Share the AI workflows that worked and the ones that did not with peers, so good patterns spread across product management.

Requirements

• At least 8 years of product management experience, including direct ownership of a workflow or operations product that sits between a sales or service organization and its customers, in a B2B, marketplace, or enterprise setting.
• Evidence of shipping AI or automation into an expert human workflow with guardrails, and of recognizing when automation is the wrong choice.
• Experience integrating products across systems of record such as CRM, ERP, file storage, and messaging, and a working understanding of how agents connect to those systems through tool protocols like MCP.
• Experience redesigning how requests enter a business across email, text, forms, APIs, and agents, with evidence of the drop-off you removed.
• Hands-on fluency with Cursor, Claude, or similar agentic tools, plus a clear view on evaluating large language model (LLM) features before and after launch. You can show where AI changed a team decision, not only where it saved time.
• Strong analytical skills: you write your own SQL, have built your own dashboards, and have caught a faulty metric before it misled a decision.
• Operational curiosity: you learn how a process works by sitting with the people who run it, and you earn their trust before changing their tools.
• Excellent written communication, so that an operations lead, an engineer, and an executive can all act on the same document.
• Ability to turn a loosely defined problem into a testable hypothesis and plan under ambiguity.
• Bachelor's degree in Business, Computer Science, Engineering, or a related field.
• Preferred, not required: experience in manufacturing, supply chain, pricing, or quoting, or a demonstrated ability to learn an unfamiliar operational domain quickly.
• Strong signals include a redesigned request-to-fulfillment funnel across a sales team, a service team, and their customers, with the channels, removed steps, before and after numbers, and adoption results.
• Strong signals also include building or owning an integration between an AI agent and a system of record, with a clear account of what the agent could do, what it had to ask a human, and how you detected its errors.
• Strong signals also include shipping an AI or machine learning (ML) feature into a human-in-the-loop process, with defined evaluation criteria, known failure modes, and post-launch monitoring.
• Strong signals also include a case where your team decided differently because of an AI-driven workflow, such as continuous customer signal, prototype-first decisions, or evaluation criteria written before the build.

Conditions

Hybrid schedule of 3 days a week at the Waltham, MA or North Bethesda, MD office.

Reports to the head of P&M product within the Pricing and Manufacturability group.

The base pay range is not yet specified in the posting; total compensation includes base pay, bonus, and equity.

The company is an equal opportunity employer and considers all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status.

Benefits

• Medical, dental, and vision insurance.
• 401(k) match.
• Life and disability insurance.
• Generous paid time off, including vacation, sick leave, floating and fixed holidays, and maternity and bonding leave.
• Employee assistance program and other wellbeing resources.