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About This Role
What We Need
Corpay is currently looking to hire an AI Visibility Program Manager within our organization. This position is located in our Buckhead, Atlanta, GA office. In this role, you will own and improve the AI visibility of an assigned Corpay business, increasing how often Corpay is discovered, cited, mentioned, reviewed, and recommended across AI platforms and the third-party sources that influence them. You will partner directly with the business Senior Vice President of Strategic Marketing and regularly collaborate with Marketing, Product, Sales, Brand, Media, PR, Digital, Analytics, and external partners.
How We Work
As an AI Visibility Program Manager, Corpay will set you up for success by providing:
- Assigned workspace in our Buckhead, Atlanta, GA office (5 days a week onsite)
- Company-issued equipment
Role Responsibilities
The responsibilities of the role will include:
- Owning the AI visibility strategy and operating plan for an assigned Corpay business
- Tracking Corpay and competitor performance across leading AI platforms, including mentions, recommendations, citations, share of voice, and priority buyer prompts
- Diagnosing visibility gaps, prioritizing actions most likely to improve performance, and maintaining a focused roadmap across content, reviews, placements, and technical opportunities
- Reviewing results and priorities regularly with the business General Manager and senior Marketing leadership while testing and adapting as AI discovery evolves
- Developing a content agenda mapped to the questions buyers ask AI platforms and competitive gaps identified through analysis
- Partnering with agency reporters to interview executives, product experts, customers, and subject matter experts; mining Corpay data and market research for insights; and managing high-quality articles, research, buyer guides, and thought leadership
- Writing or editing content directly when needed to ensure it is differentiated, credible, and useful enough to earn citations, mentions, and third-party pickup
- Partnering with the Media team to pursue priority third-party opportunities by providing compelling stories, proprietary data, executives, and expert perspectives for outreach
- Working with Media, PR, paid media, affiliate, analyst relations, and other partners to determine distribution approaches and accelerate priority placements
- Owning the strategy for increasing the quantity, quality, freshness, and visibility of customer reviews
- Improving Corpay's presence and positioning across relevant review, comparison, and recommendation platforms
- Partnering with Sales, Customer Success, Marketing, and business leadership to build a sustainable pipeline of customer advocates and using review insights to strengthen messaging and competitive positioning
- Serving as the central owner of the AI visibility agenda for the assigned business and building a strong working relationship with the General Manager and business leadership team
- Coordinating cross-functional work, establishing clear owners and timelines, removing blockers, and keeping initiatives moving from analysis through execution
- Operating with a strong bias toward action, measurable outcomes, and continuous improvement
Qualifications & Skills
- Meaningful hands-on experience using generative AI, LLMs, AI search, AIO/GEO, or related technologies in a marketing, research, editorial, consulting, digital, or business environment
- Strong understanding of how AI platforms discover, retrieve, cite, and recommend information, with practical experience using tools such as ChatGPT, Gemini, Claude, Perplexity, Copilot, or similar platforms
- Strong analytical skills with the ability to turn prompt, citation, competitive, content, or digital-performance data into clear priorities and actions
- Excellent writing, editing, interviewing, and research skills with the ability to make complex subjects clear and compelling
- Demonstrated ability to take an ambiguous problem, develop a strategy, and independently drive it through execution to a measurable result
- Commercial mindset, persistence, and confidence working with General Managers, executives, customers, partners, and cross-functional stakeholders
- Strong program-management skills with the ability to manage multiple priorities, stakeholders, deadlines, and external partners
- Experience in journalism, consulting, product marketing, content strategy, digital strategy, research, communications, or growth marketing
- B2B fintech, payments, SaaS, or financial-services experience preferred
- Bachelor's degree in Marketing, Journalism, Business, Communications, or a related field preferred
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
#LI-Corpay
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Corpay AI Hiring
Corpay has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span Peachtree Corners, GA, US, Atlanta, GA, US, Atlanta, GA.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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