Staff Product Manager, Agentic Quoting Tools

Description

The company runs a digital marketplace that connects buyers, including teams at Fortune 1000 companies, with manufacturers, giving manufacturers more work and buyers access to global manufacturing capacity.

The team is hiring a Staff Product Manager to own the request-to-quote experience from start to finish. The role sits between Sales and the quoting teams and focuses on the most important customers.

You will rework how a request becomes a priced quote across every channel it arrives through, every handoff between people and systems, and every step that can run without a person. That includes agents that read requests and build quotes, Model Context Protocol (MCP) integrations connecting agents to quoting systems and the Enterprise Resource Planning (ERP) platform, and internal tools where staff review agent output and reject what should not go through.

Success means customers get an accurate, actionable quote faster and with less effort.

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 build your own roadmap from unclear signals.

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. Know the internal steps well enough to 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 which requests an agent reads, which go to a person, and which customers can serve themselves. Make sure a request reaches a human already structured, complete, and partly priced.
• 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 stays with a human. Define success and failure criteria for each AI feature before engineering starts, and keep those criteria in force through launch and afterward.
• Own the integration layer that lets agents and people pass work between Salesforce, quoting systems, file storage, and the ERP. This covers 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 quote review, pricing, and quote summary tools. Measure adoption: a tool people avoid has failed, whatever was shipped.
• Instrument every human pricing decision, including overrides, margin changes, and lead-time adjustments, as structured data that improves future predictions and agent runs. Write your own queries and dashboards, and specify the events you need in the requirements document.
• Run discovery continuously. Work alongside quoters and account managers, review inbound email and text queues, watch session recordings, and use AI tools to synthesize findings. Check signals against live customer and staff conversations before acting. Use a working prototype tested with real users as the default basis for build decisions.
• Set a 6 to 12 month agentic quoting roadmap, sequenced by customer impact and speed. Align Engineering, Data Science, Sales, Quoting Operations, and Finance. Report progress against named outcomes to technical and non-technical audiences.
• Work spec-first with AI agents. Read relevant systems with agentic tools, ask sharper feasibility questions, and write specs an engineer can give to a coding agent. Prototype in Cursor, Claude, and Figma Make before writing requirements.
• Share AI workflows that worked and those that failed with other product managers, and make your methods visible in their work.

Requirements

• At least 8 years of product management experience, including direct ownership of a workflow or operations product 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 process 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 through tool protocols such as 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 use of 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 analytics skills: you write your own SQL, have built your own dashboard, and have caught a broken metric before it drove a bad decision.
• Operational curiosity: you learn how a process works by working alongside the people who run it, and earn their trust before changing their tools.
• Clear written communication that lets an operations lead, an engineer, and an executive work from the same document.
• Comfort turning a vague problem into a testable hypothesis and a plan.
• A bachelor's degree in Business, Computer Science, Engineering, or a related field.
• Strong signals include a request-to-fulfillment funnel you redesigned across sales, service, and customers, with channels, removed steps, before and after numbers, and adoption results.
• Strong signals also include an integration you built or owned between an AI agent and a system of record, where you can explain what the agent could do, what it had to escalate, and how you detected errors.
• Strong signals also include an AI or machine learning (ML) feature you shipped into a human-in-the-loop process, with its evaluation criteria, failure modes, and post-launch monitoring.
• Strong signals also include a case where an AI-driven workflow you built changed a team decision, such as acting on continuous customer signal, prototyping first, or writing evaluation criteria before building.
• Experience in manufacturing, supply chain, pricing, or quoting, or a demonstrated ability to learn an unfamiliar operational domain quickly.

Conditions

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

The role moves at startup pace inside a public company. You will get a metric and an open problem rather than a scoped plan, and you will be expected to go quickly from hypothesis to a shipped test. It suits people who work well from a clear brief and a stable backlog poorly.

The estimated base salary for new hires is $180,000 to $207,000 per year, plus bonus, depending on job-related skills, experience, and location. The total package includes base, bonus, and equity.

The company is an equal opportunity employer and considers all applicants regardless of 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.