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About This Role
What We Need
Corpay is looking to hire a Manager – AP AI Strategy \& Execution within our Corpay Payables division. This position sits within the AP Automation line of business and will support priorities across Corpay Payables. The role is based in Atlanta, GA and is expected to work at least three days per week from Global Corpay Headquarters in Buckhead.
We are expanding our strategy and execution capabilities to help the Payables organization achieve aggressive growth ambitions while accelerating the practical use of AI across AP Automation. This leader will drive strategic top\-line growth, identify process optimization opportunities, and translate AI\-enabled ideas into measurable business impact.
The Manager will work closely with senior management, executives, product, operations, sales, finance, and technology partners. The role requires strong analytical, cross\-functional, presentation, communication, and execution skills, along with significant interest in AI and hands\-on experience building AI agents or agent\-based workflows.
To be successful, this person must be able to frame ambiguous business problems, validate innovative ideas with data, and convert recommendations into clear execution plans. The Manager will present researched facts, highlight key takeaways, advise on approach, and help resolve issues that could slow execution. The position will report directly to the President \& GM, AP Automation Strategy.
How We Work
As a Manager, AP AI Strategy \& Execution, you are expected to work in a hybrid environment based in Atlanta, GA. Corpay will set you up for success by providing:
- Company\-issued equipment and access to required systems.
- An expected working model of at least three days per week in Global Corpay Headquarters in Buckhead.
- Opportunities to partner with cross\-functional stakeholders across Corpay Payables and corporate teams.
Role Responsibilities
- Driving strategic initiatives from problem definition through execution, stakeholder alignment, measurement, and adoption.
- Identifying, evaluating, and prioritizing AI\-enabled opportunities that improve AP Automation growth, operating efficiency, customer experience, or scalability.
- Building, testing, or helping deploy practical AI agents and agent\-based workflows, with a focus on process optimization and measurable business outcomes.
- Providing in\-depth analysis and insights to advise senior management and executives when making critical business decisions.
- Partnering with executives, senior management, and teams across the organization to gather business requirements, data inputs, and stakeholder context.
- Analyzing, tracking, and measuring the impact of launched strategic initiatives, including revenue, efficiency, adoption, and customer impact.
- Identifying and recommending improvements to current business processes, with a focus on revenue growth, process quality, automation, and speed of execution.
- Performing structured revenue analysis and commentary on key variances versus targets, prior months, and prior years, including recommended actions.
- Defining key metrics and analyze trends that support organic customer revenue retention and growth.
- Introducing new analysis, reporting formats, operating cadences, and AI\-enabled tools that provide better insight into the business.
- Reprioritizing based on new findings to ensure the organization is focused on the highest\-value opportunities.
Qualifications \& Skills
- 5\+ years of experience in strategic planning, data analytics, business positioning, operations strategy, or a related field.
- Experience in program management, business analysis, financial planning, or strategic initiative execution.
- Hands\-on experience building AI agents, agent\-based workflows, automations, or AI\-supported productivity tools. Experience applying agents to process optimization is a strong plus.
- Significant interest in AI, automation, and the practical application of emerging technologies to improve business processes.
- Entrepreneurial mindset: able to think big, develop innovative ideas, test assumptions, and execute with discipline.
- Strong problem\-solving skills, including the ability to break down ambiguous issues, identify root causes, and align stakeholders on action plans.
- Data savvy, with effective data processing, analysis, and visualization capabilities.
- Exceptional capabilities in the Microsoft 365 product suite.
- Proficiency in Microsoft SQL Server Management Studio (SSMS).
- Proficiency in business intelligence tools such as Power BI or IBM Analytics.
- Superb communication skills, including the ability to hold influential conversations and discussions.
- Develop strong working relationships with cross\-functional stakeholders.
- Build executive confidence in new markets, product features, operational opportunities, or AI\-enabled recommendations.
- Deliver clear proposals, polished presentations, and actionable insights backed by analysis and research.
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
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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Corpay AI Hiring
Corpay has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, 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/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 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).
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 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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