Engagement Manager

Posted Yesterday
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San Francisco, CA, USA
In-Office
Senior level
Artificial Intelligence • Logistics • Software • Automation
The Role
Own enterprise sales engagements from discovery through signed MSA, including qualification, stakeholder management, ROI business cases, procurement, security, and legal negotiations. Translate operational challenges into AI-agent opportunities, influence senior buyers, and provide customer feedback to product, engineering, and go-to-market teams. Close enterprise pilots and help create the company’s sales qualification and engagement playbook.
Summary Generated by Built In

Location: Onsite — San Francisco, CA

Employment Type: Full-Time

Job Title: Engagement Manager

 

Company Overview

We're building the AI workforce for the physical economy — AI agents that replace the manual coordination layer running on emails, spreadsheets, TMS, ERP, and phone calls at companies that move, make, and deliver physical things. Our agents handle complex multi-day workflows, learn from feedback, and reach 90%+ autonomous execution in production.

 

Our founder Obaid Khan co-founded Motive — scaled it to $350M+ in revenue and a $5B+ valuation. We're going after the next big problem in the same market. Early team but we have multiple customers including Fortune 500. We move fast and hold a high bar.

 

About the Role

Runbook builds AI agents that automate the manual coordination work running operations teams in the physical economy — transportation, field services, manufacturing, retail. Founded by Obaid Khan (founder of Motive) with a team that's seen this market from the inside.

Engagement Managers run the front of the deal: discovery, qualification, business case, and close. You partner with our agent product managers on what we build for a customer, and hand off to our deployment team after signature. You're early on the GTM team and you'll shape the playbook the next ten EMs run. Reports to the CEO, who previously scaled a multi-billion-dollar business.

You see what's possible for customers before anyone else does, and you bring that signal back to product, engineering, and GTM.

 

What you'll do

  • Run the deal. Own the cycle from first conversation to signed MSA across enterprise accounts. Drive pace, sequence stakeholders, and keep the deal alive through procurement, security, and legal.

  • Get to the real problem. Lead discovery from strategic objectives to top use cases to workflow automation candidates. Pull a real picture of how the customer's operation runs today — systems, handoffs, the people doing the work, where it breaks. Pressure-test which workflows we should actually go after.

  • Find & build business cases with the customer. Build ROI cases by extracting context from operators and finance. Enable your champions to vouch for our solution internally.

  • Run the room. Hold your own with COOs, CIOs, and VPs of Operations. Translate AI agents into operational language, and operational pain into something Product can build against.

  • Bring back signal. What customers ask for that we don't have, what works in the room and what doesn't, where the next deal lives. The patterns you spot become roadmap, positioning, and pipeline.

What success looks like at 6 months

  • Six enterprise pilots closed.

  • A qualification framework the next EMs run against — not because we asked you to write one, because you got tired of re-deriving it.

  • Product knows what to build next because of the deals you ran.

What we're looking for

  • 4–10 years across some combination of management consulting, investment banking, private equity, strategy & ops, or enterprise sales. You've been in front of senior buyers and you've owned outcomes.

  • Competitive paranoia. You're always thinking about how to make the deal go faster and where it can fall apart. You see around corners and help customers and teammates see around them too.

  • High pain tolerance. Enterprise procurement, security review, legal redlines, stalled champions, reorg'd buyers — you stay in motion. You don't need a playbook to be written for you; you write the next version of it.

  • Business case fluency. You can sit with an operator for an hour and walk out with the inputs to a defensible ROI. You know what a CFO will push on and you've already accounted for it.

  • Technical intuition. You don't need to write code. You do need to understand how AI agents work well enough to explain why one approach fits a workflow and another doesn't — and to know when to pull in our agent product managers.

  • Clear writing and clear speaking. Short sentences. Real numbers. No jargon padding.

Nice to have

  • Current startup experience. You're in a Series A–D environment now and you're fluent in the pace and the ambiguity. Not required — we'll teach the Runbook part — but it shortens the ramp.

  • Domain. Logistics, transportation, field services, manufacturing, or retail operations. Helpful, not required.

How we work

In-office. High velocity. We hire people who would rather ship than deliberate, and we trust them to figure out the right thing to do without a 12-step playbook.

Skills Required

  • 4–10 years of experience across management consulting, investment banking, private equity, strategy and operations, or enterprise sales
  • Experience working with senior buyers and owning business outcomes
  • Ability to build defensible ROI and business cases with operators and finance stakeholders
  • Ability to understand and explain AI-agent approaches in operational workflows
  • Clear written and verbal communication skills
  • Current startup experience in a Series A–D environment
  • Experience in logistics, transportation, field services, manufacturing, or retail operations
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The Company
22 Employees
Year Founded: 2025

What We Do

Runbook builds an AI agent platform for the physical economy, serving logistics companies, manufacturers, construction firms, and field-service operators. Its agents automate manual operational work and coordination across existing tools—including email, spreadsheets, TMS, ERP, and phone calls—and handle complex, multi-day workflows. They learn from feedback and can reach high levels of autonomous execution. The company aims to reduce operational toil while giving teams reliable, controlled automation.

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