Staff Product Manager, Agentic AI

Why project44?

At project44, we believe in better. We challenge the status quo because we know a better supply chain isn’t just possible—it’s essential. Better for our customers. Better for their business. Better for the world.

With our Decision Intelligence Platform, Movement, we’re redefining how global supply chains operate. By transforming fragmented logistics data into real-time, AI-powered insights, we empower companies to connect instantly, see clearly, act decisively, and automate intelligently. Our Supply Chain AI enhances visibility, drives smarter execution, and unlocks next-gen applications that keep businesses moving forward.

In-office Commitment

We are looking for candidates who are enthusiastic and committed to joining our team on-site, in our beautiful headquarters four days a week. Together, we’re building something extraordinarily learn, grow, and thrive in our fast-paced, transformative environment.

The opportunity

Autopilot is project44’s no-code platform for deploying purpose-built workflows with AI agents into the mission-critical workflows supply chain teams run every day — calling carriers, collecting missing data, reconciling documents, resolving exceptions, and running mini-bids within guardrails.

What You’ll Do

• Lead with customers and research

  • Own the customer problem before the solution. Every workflow starts from a clearly stated customer problem, who is impacted (planners, logistics managers, appointment and yard managers, carrier dispatch, drivers), and when it occurs — not from a feature idea.
  • Run primary research continuously: customer interviews, ride-alongs with operations teams, design-partner pilots, Customer Advisory Board (CAB) validation sessions, win/loss and churn intake reviews, and direct analysis of platform behavior.
  • Recruit and manage design partners for shadow-mode pilots — where the agent logs what it would do before it acts — to establish honest baselines and earn trust ahead of live deployment.
  • Be the domain and product expert in customer-facing settings: demos, executive briefings, CAB, and conferences.
  • Translate what you hear into a prioritized, defensible roadmap.

• Drive AI innovation

  • Push the frontier of what agents can safely do in production: autonomous voice and email outreach, document parsing and reconciliation, reason-code classification and write-back, and multi-agent workflows that coordinate several agents across a single business outcome.
  • Move workflows up the maturity curve — from supporting a user (surfacing a signal), to augmenting them (taking one action in a human-run flow), to automating a complete work task (a multi-agent workflow that runs the whole job end to end).
  • Define, for each use case, the bar a workflow must clear to graduate to the next stage.
  • Make workflows authorable and executable in natural language through Mo, project44’s AI Supply Chain Analyst — so a user can describe a workflow conversationally and have Autopilot stand it up, run it, and report back.
  • Own the Autopilot side of the integration and the mapping from natural language to triggers, conditions, and actions, partnering with the Mo product management team.
  • Design for trust: configurable controls, transparent logic, audit trails, intervention points, hallucination guards, and throttles tuned per use case.
  • Decide where humans stay in the loop and where agents can act autonomously.
  • Partner with engineering, applied AI, and design on workflow architecture — triggers, conditions, actions, contact-resolution strategy, retry and cadence logic, and closure semantics — and on the tooling that lets us scale workflow production toward one per day.
  • Stay ahead of a fast-moving competitive field of agentic logistics startups; know precisely why project44’s network and context are the durable advantage and build the product to exploit it.

• Write outcomes-based requirements

  • Author crisp PRDs along with rapid prototypes. framed around goals and non-goals, explicit success/failure metrics, and leading and lagging indicators — not feature checklists.
  • (A workflow marked “completed” is not the same as a workflow that succeeded; you’ll define success by the outcome it produced.)
  • Specify configurability deliberately: what is a sensible pilot default versus what each tenant must be able to tune (thresholds, conditions, allow/block lists, cadence, channels).
  • Maintain a prioritized backlog across the three surfaces and sequence it against customer value, trust gating, and business results.
  • Synthesize complex, multi-mode use cases (FTL, LTL, ocean, drayage, intermodal) into an actionable roadmap.
  • Hold the gating bar: data availability, provider readiness, legal/compliance review (e.g., TCPA and calling-hours guards for outbound contact), and human-QA thresholds before write-back or autonomous action is unlocked.

• Measure and report outcomes

  • Define the metric model for every workflow before it ships, and instrument it: validation/completion rates, outcome classification confidence, reduction in manual coordination and exception handling, accuracy improvements (e.g., ETA MAPE/MAE), freight-spend and disruption-cost impact, response and reach rates, and adoption.
  • Own AI Agent Analytics and the LunaIntel / LunaVoice reporting experience so customers can see agent performance and outcomes by use case and persona — and so we can prove ROI in renewals, QBRs, and executive reviews.
  • Run the outcome loop: turn what the dashboards reveal (non-response patterns, ambiguous outcomes, value by segment) back into roadmap decisions, throttle changes, and the next workflows to build.

• Produce high-quality, executive-ready deliverables

  • Investment memos, roadmap reviews, launch readouts, and enablement — with the same attention to detail you bring to the product.

What we're looking for

• 5+ years in product management (more for Principal level), including hands-on ownership of a technical, data-rich, or AI/ML product through the full lifecycle — discovery, definition, GTM, and iteration.

• Demonstrated customer obsession: a track record of grounding product decisions in direct research and of representing the customer credibly to engineering and to executives.

• Fluency with AI / agentic systems — LLM-powered agents, orchestration, evaluation, human-in-the-loop design, and the practical realities of getting non-deterministic software production-ready and trusted.