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About This Role
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.
Headquartered in Chicago, IL with a 2nd HQ in Bengaluru, India we are powered by a diverse global team that is tackling the toughest logistics challenges with innovation, urgency, and purpose.
AI at project44
We expect every project44 team member, regardless of role or function, to actively leverage AI in their day\-to\-day work. Whether you're building product, serving customers, managing people, or running operations, AI is a tool you're expected to use with intent, curiosity, and judgment. We don't expect everyone to be a data scientist. We do expect everyone to be an intelligent user of AI: able to identify where it adds value, direct it effectively, evaluate outputs critically, and govern it responsibly. We invest in our team's AI fluency because we believe it's a competitive advantage for every person at project44, not just our engineers.
If you're driven to solve meaningful problems, leverage AI to scale rapidly, drive impact daily, and be part of a high\-performance team – we should talk.
In\-office Commitment: Our office is where ideas spark, connections thrive, and innovation comes alive. 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. It is the product layer that sits on top of our agent portfolio (Freight Procurement, Disruption Management, Network Operations, Execution Recovery, Stockout Risk, and more) and gives customers the steering wheel: configurable triggers, transparent logic, audit history, and human checkpoints at every critical step.
This role owns three connected surfaces:
- AI Workflows — the growing library of packaged, customer\-deployable agent workflows (e.g., validate early ETAs, late\-shipment carrier outreach, collect missing milestones, stale\-position investigation). You define which jobs we automate next, in what sequence, and to what standard.
- AI Agent Workflow Manager (fka Autopilot) — the no\-code configurator itself: the trigger / condition / action canvas, workflow variants, multi\-agent orchestration, human\-in\-the\-loop controls, and the build\-and\-deploy experience that lets customers (and our own teams) ship workflows without engineering.
- AI Agent Analytics \& Reporting — the measurement layer (AI Agent Analytics, LunaIntel and LunaVoice dashboards, collaboration and carrier\-performance reporting) that proves outcomes by use case and persona, exposes agent performance to customers, and closes the loop back into the roadmap.
The arc you'll drive: from support, to augment, to automate. The first generation of AI workflows *supported* users — surfacing the right signal at the right moment. The current generation *augments* them — taking a discrete action inside a human\-run workflow (make the call, collect the milestone, draft the response). The mandate for this role is to push to the next stage: multi\-agent workflows that automate complete work tasks end to end — coordinating several agents across a full job (e.g., detect reach out confirm reconcile write back close) so an entire operational task runs without a human in the loop, while staying transparent, auditable, and reversible. You'll own that progression use case by use case, deciding when a workflow is ready to move from augmenting a person to automating the task outright.
Natural\-language workflow authoring with Mo. Mo is project44's AI Supply Chain Analyst — a conversational agent embedded in Movement that lets users ask questions of their own data in natural language (NLSQL / NLAPI), grounded in their shipments, business rules, and carrier history. Mo's roadmap runs from *search* to *analyze* to *act*, and Autopilot is the "act." A core part of this role is making Autopilot workflows authorable and executable through Mo in plain language — "set up a workflow that calls the carrier whenever an FTL shipment is running two hours late and update the ETA" — and surfacing Autopilot task history and run status back inside Mo's answers. You'll own the Autopilot side of that integration and partner closely with the Mo product management team on the shared experience, the trigger/condition/action vocabulary Mo maps natural language onto, and the guardrails for letting a conversational agent stand up an autonomous workflow.
We're moving fast — from shipping roughly one workflow a week to one a day — and the bar is high: very little vibe\-coded software is production\-ready, and customers only adopt agents they trust. You'll set the throttle, use case by use case, so speed never outruns trust.
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 success looks like in the first year
- A steadily expanding, high\-trust AI Workflow library shipping at an increasing cadence, with each workflow tied to a measured customer outcome.
- At least one flagship multi\-agent workflow that fully automates a complete operational task end to end — moving a meaningful job from human\-run to agent\-run without eroding trust — with a clear, repeatable bar for graduating future workflows from support, to augment, to automate.
- Autopilot workflows authorable and executable through Mo in natural language, with adoption measured by workflows created and triggered via Mo — shipped in partnership with the Mo product management team.
- Measurable business impact from deployed agents — in the range project44 already demonstrates today: meaningful reductions in freight spend and manual coordination, double\-digit reductions in manual exception handling, materially improved ETA accuracy, and faster sourcing cycles.
- AI Agent Analytics adopted as the system of record for agent performance — used by customers, CX, and the executive team alike.
- Rising adoption and trust among Autopilot customers, reflected in NPS and renewal/expansion, with no trust\-eroding incidents from agents acting beyond their guardrails.
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.
- An outcomes\-first operating style: you write requirements as goals, non\-goals, and success metrics, and you instrument and report on impact rather than output.
- Strong analytical skills — comfort defining metrics, working in dashboards and warehouse data (e.g., Snowflake\-backed analysis), and reasoning quantitatively about agent performance and ROI.
- Excellent written and verbal communication; able to influence engineers, designers, sales, partners, customers, and corporate leadership, and to produce polished executive deliverables.
- Comfort with ambiguity and speed; able to set the right throttle so the team ships fast without outrunning customer trust.
Nice to have
- Logistics, supply chain, or transportation domain experience (visibility, TMS, YMS, procurement, carrier networks).
- Experience building no\-code / workflow\-builder, automation, or analytics\-and\-reporting products.
- Experience with conversational / natural\-language interfaces (LLM chat, NLSQL / NLAPI) and shipping a shared experience across two product teams.
- Familiarity with voice/communications platforms or outbound\-contact compliance (TCPA, calling\-hours rules).
- Prior work running design\-partner programs, CABs, or beta/pilot motions for net\-new product categories.
- + In\-office Commitment: Employees are expected to contribute to our collaborative culture by working in the office FOUR days weekly
Diversity \& Inclusion
We're designing the future of how the world moves and is connected through trade and global supply chains. We can only deliver a truly world\-class product and experience if our teams are as diverse and unique as the communities we are building for. It's up to us to create a company where anyone can bring their authentic self to work every day. We're constantly working to improve, and we accept our responsibility to elevate the voices left in the margins. It's on every one of us.
Our focus on inclusion manifests in the way we hire, the customers we serve, and the regions we prioritize. We're building a company that every one of us at project44 is proud to work for: a company that celebrates you for being you.
We pride ourselves on celebrating everyone — project44 is an equal opportunity employer actively working on creating a diverse and inclusive work environment where underrepresented groups can thrive. If you share our values and our passion for helping the way the world moves, we'd love to review your application!
For any accommodations needed during the hiring process, please email recruiting@project44\.com. Even if you don't meet 100% of the above qualifications, you should still seriously consider applying. Studies show that you can still be considered for a role if you meet just 50% of the role's requirements.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At project44, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
project44 AI Hiring
project44 has 1 open AI role right now. They're hiring across AI Product Manager. Based in Palo Alto, CA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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