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About This Role
What We Need
CORPAY is currently looking to hire a VP, Airlines Product Integration Strategy, within our Airlines Product division. This position falls under our Lodging line of business and is located in Brentwood or Atlanta. In this role, you will oversee the strategy to continue expanding the connectivity of TAC’s products and platforms into the complex Airlines technology ecosystem. This ecosystem includes Airline Crew Management Systems, Operations Systems, as well as suppliers and partners in the hotel, transportation, and payments industry.
Operating within the Product organization, this role provides technical leadership, architectural guidance, and commercial enablement supporting the full lifecycle from pre-sales engagement through post-sale implementation and long-term product strategy. This role reports directly to Corpay Lodging’s Chief Product Officer.
How We Work
As a VP, you will be expected to work in the office. CORPAY will set you up for success by providing:
- Assigned workspace in our office.
- Company-issued equipment + remote access
Role Responsibilities
The responsibilities of the role will include:
Pre-Sales & Post-Sales Enablement
- Acting as an executive technical advisor during pre-contract discussions, solution discovery, and deal shaping.
- Providing technical validation, feasibility assessments, and solution positioning to accelerate deal closure.
- Providing post-sale technical support during implementation to ensure successful onboarding and Go-Live.
- Translating contractual and commercial commitments into clear technical requirements for delivery teams.
Partner Technical Management
- Acting as an executive liaison between Airlines, TAC, and strategic suppliers in the hotel, transportation, and payments ecosystem.
- Ensuring accurate exchange and governance of technical and business requirements critical to the integration between TAC and strategic suppliers to support Airlines’ operations.
- Driving consistency, security, and accountability across multi-party integrations.
Product & Platform Strategy
- Supporting strategic initiatives and product innovation through technical feasibility, solution design, and commercial alignment.
- Driving measurable improvements in deal velocity, implementation outcomes, and time-to-market.
Success Metrics
- Driving Revenue through solution design and technical sales support
- Reducing sales cycle time for complex, solution-driven deals
- Faster and more predictable implementation timelines
- Improving customer adoption and platform scalability
- Reducing post-implementation issues and support
Qualifications & Skills
- Deep expertise in aviation, hospitality, and payments technology ecosystems
- Leadership experience in solution design, technical product management, and commercial enablement roles
- Strong technical fluency across APIs, integrations, and enterprise platforms
- Proven success delivering large-scale, cloud-based platforms with complex integrations and API ecosystems
- Demonstrated ability to indirectly lead distributed teams and deliver consistent results across time zones.
- Experience with API management, partner onboarding, and developer experience platforms.
- Results-driven leader who balances strategy with execution and accountability.
- Influencer and culture builder—able to set expectations, inspire teams, and build alignment across the organization.
- Strong communicator with a collaborative mindset and customer-first orientation.
Benefits & Perks
- Medical, Dental & Vision benefits available the 1st month after hire
- Automatic enrollment into our 401k 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
Our Company & Purpose
CORPAY is a global leader in business payments, laser focused on developing smarter ways for businesses to pay their expenses. Since 2000, CORPAY has developed innovative digital solutions that help businesses better track, manage, and pay their expenses. Today, CORPAY is an S&P 500 company with hundreds of thousands of customers using our products in over 100 countries. Companies of all sizes, industries and geographies rely on our product portfolio to manage spending more quickly, efficiently and securely than ever before.
We embrace a culture grounded in five key values: integrity, collaboration, innovation, execution and people. These values offer you the opportunity to ‘thrive & grow’ through career development, volunteer, community, and wellness initiatives. This allows you to create a balance between professional goals and personal achievement.
CORPAY is also committed to building and nurturing a culture of diversity, inclusion, equality, and belonging by:
- Welcoming people of different backgrounds, cultures, ethnicities, genders, and sexual orientations.
- Empowering our people to share their experiences and ideas through open forums and individual conversations; and
- Valuing each person’s unique perspectives and individual contributions.
Embracing diversity enables our people to “make the difference” as CORPAY and its more than 8,000 employees continue to shape the future of global payments. Learn more by visiting www.CORPAY.com or following CORPAY on LinkedIn.
Equal Opportunity/Affirmative Action Employer
CORPAY is an Equal Opportunity Employer. CORPAY provides equal employment opportunities to all employees and applicants without regard to race, color, gender (including pregnancy), religion, national origin, ancestry, disability, age, sexual orientation, gender identity or expression, marital status, language, ancestry, genetic information, veteran and/or military status or any other group status protected by federal or local law. If you require reasonable accommodation for the application and/or interview process, please notify a representative of the Human Resources Department.
For more information about our commitment to equal employment opportunity and pay transparency, please click the following links: EEO and Pay Transparency.
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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $154,000 based on 8,743 positions with disclosed compensation.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Corpay AI Hiring
Corpay has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Brentwood, TN, US, Atlanta, GA, US.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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