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What We Need
Corpay is seeking an experienced Product Manager – AI to join our Commercial Cards business within the Corporate Payments division, and the role is remote. The Product Manager will serve as the voice of the customer, translating business needs and market opportunities into innovative product capabilities. This role will develop compelling value propositions, support go\-to\-market initiatives, and engage directly with customers and commercial teams to ensure our Intelligent Administrator solutions continue to meet evolving client needs. The Product Manager will work with the President of Commercial Card Services and cross\-functional leaders to shape product strategy, deliver customer\-centric solutions, and accelerate market adoption.
How We Work
As a Product Manager – AI, Corpay will set you up for success by providing:
- Home office set up
- Company\-issued equipment
Role Responsibilities
The responsibilities of the role will include:
- Partner with executive leadership to understand the long\-term strategic priorities of the Commercial Cards business and translate them into a customer\-centric product vision and roadmap.
- Lead discovery, design, and delivery of AI\-powered experiences and intelligent agents that help customers complete complex administrative workflows more efficiently.
- Leverage Artificial Intelligence (AI), automation, and emerging technologies to identify opportunities that improve customer experience, streamline workflows, and create measurable business value.
- Conduct continuous market research, competitive analysis, and customer discovery to understand industry trends, evolving client needs, and opportunities for product innovation.
- Meet directly with customers, prospects, partners, and field sales teams to validate product concepts, gather feedback, and ensure solutions solve real\-world business challenges.
- Develop compelling product positioning, value propositions, and messaging that clearly articulate the business benefits of Intelligent Administrator capabilities to customers and internal stakeholders.
- Translate strategic business objectives and customer insights into clearly defined product requirements, user stories, prototypes, and functional specifications.
- Create prototypes, proof\-of\-concepts, and early product designs to validate ideas and accelerate stakeholder alignment before development begins.
- Prioritize product initiatives based on customer impact, commercial value, technical feasibility, and alignment with Corpay's broader Corporate Payments strategy.
- Lead cross\-functional collaboration across Product, Engineering, UX, AI/ML, Sales, Marketing, Operations, Customer Success, and Implementation teams to successfully deliver product enhancements.
- Coordinate with multiple Product Owners and Agile development teams to ensure product capabilities are built according to strategic priorities and deliver maximum business value.
- Balance competing business priorities across multiple stakeholders while maintaining focus on long\-term strategic initiatives and customer outcomes.
- Define product success metrics, monitor adoption and customer engagement, and use data\-driven insights to continuously improve product performance.
- Identify opportunities to incorporate Generative AI, predictive analytics, intelligent automation, and workflow optimization into existing and future product capabilities.
- Support go\-to\-market planning by partnering with Marketing and Sales to develop product launches, customer communications, training materials, and sales enablement resources.
- Serve as the internal and external product evangelist by presenting product strategy, roadmap updates, and customer success stories to executives, customers, and industry events.
Qualifications \& Skills
- Bachelor’s degree in business, Finance, Marketing, Computer Science, or a related field.
- 5\+ years of Product Management experience, preferably within FinTech, Commercial Cards, Corporate Payments, Banking, or SaaS.
- Product management experience delivering AI\-enabled, machine\-learning, intelligent automation, or Generative AI capabilities in a production environment is strongly preferred
- Hands\-on experience evaluating, prototyping, or launching AI\-powered customer experiences or intelligent agents is preferred.
- Experience managing customer\-facing B2B software or payment solutions.
- Strong understanding of Commercial Card programs, expense management, AP automation, or payment technologies is highly preferred.
- Demonstrated ability to translate customer insights into product strategy and execution.
- Experience creating product positioning, messaging, and go\-to\-market strategies.
- Excellent communication and executive presentation skills with the ability to influence stakeholders across all levels of the organization.
- Strong analytical, strategic thinking, experimentation, and problem\-solving abilities.
- Ability to partner effectively with Engineering, UX, AI/ML, Sales, Marketing, Operations, Implementation, and Customer Success to launch new capabilities.
- Experience monitoring product performance using KPIs, customer feedback, and market trends to continuously optimize the product offering.
- Ability to conduct competitive analysis and market research to identify emerging technologies and opportunities within Commercial Cards and B2B payments.
- Commitment to building AI\-enabled products that are secure, trusted, responsible, and measurable.
Benefits \& Perks
- Medical, Dental \& Vision benefits available the 1st month after hire
- Automatic enrollment into our 401(k) plan (subject to eligibility requirements)
- Virtual fitness classes offered company\-wide
- Robust PTO offerings including major holidays, vacation, sick, personal, \& volunteer time
- Employee discounts with major providers (i.e. wireless, gym, car rental, etc.)
- Philanthropic support with both local and national organizations
- Fun culture with company\-wide contests and prizes
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 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Corpay, 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 in Demand for This Role
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 $217,100 based on 471 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Corpay AI Hiring
Corpay has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Atlanta, GA, US, Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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