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About This Role
What We Need
Corpay is looking for an AI Product Owner to join our team. In this role, the Product Owner for Virtual Fleet Manager will be partnering with Product Management, Engineering, AI Engineers, UX Designers and business stakeholders to rapidly transform customer problems into AI\-powered solutions. Rather than managing a traditional Agile backlog, you'll help define product intent, collaborate in AI\-assisted design sessions, and guide development from inception through to production grade feature sets using modern AI software development practices. You will help shape how AI changes fleet management—from assisting users with recommendations today to safely automating work tomorrow.
How We Work
As an AI Product Owner you will be expected to work in a hybrid environment reporting to our Buckhead office location 4 days a week.
Corpay will set you up for success by providing:
- Assigned workspace at our Buckhead office
- Company\-issued equipment
- Formal, hands\-on training
Role Responsibilities
The responsibilities of this role will include:
Product Discovery \& Customer Outcomes
- Developing a deep understanding of fleet managers, their workflows, and operational challenges.
- Translating customer problems into clear product opportunities and measurable outcomes.
- Continuously validating ideas through customer conversations, prototypes, analytics, and experimentation.
- Balancing customer value, technical feasibility, and business priorities when making product decisions.
AI\-Enabled Product Development
- Partnering with AI Engineers to design experiences powered by Large Language Models and intelligent agents.
- Defining AI workflows, recommendations, approval experiences, and autonomous actions.
- Helping establish guardrails, confidence thresholds, and evaluation criteria for AI features.
- Continuously improving AI quality using customer feedback, production metrics, and experimentation.
- Helping determine when AI should recommend an action versus automatically performing it.
Intent\-Driven Development
- Creating Intent Documents that clearly communicate customer problems, desired outcomes, business context, and success criteria.
- Collaborating with Engineering to refine implementation approaches rather than prescribing detailed requirements.
- Focusing on the "why" and desired customer experience while allowing Engineering flexibility in execution.
- Taking features from intent to build\-ready: business rules a QA engineer ca test against, acceptance criteria without ambiguity, and success metrics defined before development begins.
- Using lightweight documentation and rapid feedback rather than extensive specifications.
AI\-Enabled Product Development
We believe AI should improve how products are built—not just what products we build.
You will be expected to:
- Use AI tools throughout product discovery, planning, documentation, analysis, and decision making.
- Leverage AI to accelerate research, synthesize customer feedback, generate requirements, analyze data, and improve communication.
- Collaborate with Engineering teams using AI\-assisted development workflows.
- Continuously evaluate new AI tools that improve team productivity and product quality.
Collaborative Development
- Participating in collaborative design and mobbing sessions with Product, Engineering, UX, and AI teams.
- Making decisions quickly through collaboration rather than formal handoffs.
- Treating AI quality as a product metric. When a conversational feature dead\-ends, a task fails, or ignores a user asking for help, find and fix the issue.
- Helping determine when AI should recommend an action versus automatically perform it.
- Supporting rapid iteration as customer learning curve evolves.
Product Success
- Defining success metrics before development begins.
- Measuring customer adoption, engagement, trust, and business impact.
- Monitoring AI quality and identify opportunities for continuous improvement.
- Partnering with Sales, Marketing, Customer Success, and Support to ensure successful product launches and customer adoption.
Qualifications \& Skills
- 4–7 years of experience building SaaS software products.
- Experience working closely with software engineering teams throughout the software development lifecycle.
- Strong customer discovery and product thinking skills.
- Excellent written and verbal communication skills with demonstrated ability to write clear, testable requirements.
- Experience making data\-informed product decisions.
- Curiosity and enthusiasm for AI technologies and modern software development practices.
Preferred
- Experience delivering AI\-powered software products.
- Experience working with Generative AI, LLMs, AI Agents, or conversational interfaces.
- Familiarity with AI Software Development Lifecycle (AI SDLC) concepts.
- Experience creating Intent Documents, product briefs, or lightweight product specifications.
- Experience using AI tools such as ChatGPT, Claude, GitHub Copilot, Cursor, Atlassian Rovo, Kiro, or similar tools in daily product work.
- Experience measuring AI product quality and customer adoption.
- Experience in Fleet Management, Financial Technology, Payments, or B2B SaaS. Familiarity with concepts like rebates, discounts, billing cycles and card networks is a strong plus.
Benefits \& Perks
- Medical, Dental \& Vision benefits available the 1st month after hire
- Automatic enrollment into our 401(k) retirement savings plan
- Virtual fitness classes offered company\-wide
- Robust PTO offerings including major holidays, vacation, sick, personal, and volunteer time
- Employee discounts with major providers (wireless, gym, car rental, and more)
- Philanthropic support with local and national organizations
- Fun culture with company\-wide contests and prizes
\#CORPAY
\#LI\-DR1
About Corpay
Corpay is a global technology organization that is leading the future of commercial payments with a culture of innovation that drives us to constantly create new and better ways to pay. Our specialized payment solutions help businesses control, simplify, and secure payment for fuel, general payables, toll and lodging expenses. Millions of people in over 80 countries around the world use our solutions for their payments.
At Corpay, we are committed to fostering an inclusive and respectful workplace where employees are valued for their diverse perspectives, experiences, and contributions. We believe that diversity, equity, and inclusion strengthen our teams, drive innovation, and support our continued success globally.
As part of our hiring process, offers of employment may be subject to the successful completion of pre\-employment screening conducted by an authorized third\-party provider, in accordance with applicable laws and Corpay policies. Screening requirements may include employment references, identity verification, criminal record checks, financial or sanctions screening, and other background checks relevant to the role and permitted by local law.
Notice to Recruitment Agencies and Search Firms: Corpay does not accept unsolicited resumes from agencies or search firms without a valid written agreement in place. Any unsolicited candidate submissions will become the property of Corpay, and no fees will be paid related to such submissions.
Learn more about Corpay: https://www.corpay.com
Transparency \& Compliance
Equal Opportunity Employer
Corpay is committed to providing equal employment opportunities to all applicants and employees. Employment decisions are made without regard to race, color, religion, sex (including pregnancy), gender, gender identity or expression, sexual orientation, national origin, ancestry, age, disability, marital status, genetic information, military or veteran status, or any other characteristic protected by applicable law. Corpay is committed to fostering an inclusive workplace where individuals are respected and valued for their diverse perspectives, experiences, and contributions. If you require reasonable accommodation during any part of the application or interview process, please notify a representative of the Human Resources Department.
Use of Artificial Intelligence in Hiring
Corpay may use artificial intelligence (AI) and other technology\-enabled tools to support certain aspects of the recruitment process, such as application screening, candidate assessment, or interview scheduling. These tools are designed to enhance efficiency, consistency, and fairness throughout the hiring process. AI tools do not make final hiring decisions. All employment decisions involve human review. Corpay is committed to the responsible use of AI, including appropriate oversight and safeguards designed to support fair and unbiased outcomes.
Candidate Privacy Notice
For information about how Corpay processes personal information during the recruitment process, please review our Candidate Privacy Notice: https://www.corpay.com/privacy\-policy.
Pay Philosophy
Corpay is committed to fair, equitable, and transparent compensation practices. Compensation decisions are based on objective, job\-related factors including skills, experience, qualifications, and market benchmarks. Where required by applicable law, salary or compensation ranges will be included in the job posting or provided prior to the interview process, where required by applicable law. Additional compensation elements such as bonuses, incentives, benefits, or variable pay may apply where applicable.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Corpay, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 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.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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