Interested in this AI/ML Engineer role at Mastercard?
Apply Now →About This Role
Our Purpose
*Mastercard powers economies and empowers people in 200\+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.*
Title and Summary
Vice President, Product Management, Agentic Commerce
Mastercard’s Agentic \& Digital Growth Products team is looking for a Vice President, Product Management, Agentic Commerce to serve as a product leader and business strategy owner for Mastercard Agent Pay and Agent Pay for Machines \- Mastercard's flagship machine commerce product – with a mission to enable AI agents and autonomous systems to transact securely at high velocity and massive scale. The ideal candidate will be highly motivated, intellectually curious, analytical, and excited by the challenge of balancing the needs of many diverse stakeholders.
Our team leads Mastercard's efforts to build and scale digital payment products that enable commerce across devices, contexts, and geographies. We work with the biggest names in tech, ecommerce and financial services to leverage Mastercard's technological capabilities to solve partner business problems, create new propositions, and ensure seamless, secure and inclusive payments.
Role
The Vice President, Product Management, Agentic Commerce will be focused on defining key strategic and commercial elements of Mastercard's strategy to enable access to digital credentials wherever AI agents are being enabled for payment.
This individual will own product strategy, roadmap, ecosystem design, commercialization, technical direction, and delivery across all workstreams required to bring to the Agentic commerce solutions to market.
They will act as the primary orchestrator across product, engineering, architecture, franchise, legal, treasury, settlement operations, regional teams, and external ecosystem partners.
Success in this endeavor will mean delivering a robust commercial strategy for the products in focus, a cutting\-edge customer and developer experience, and rapidly validating and iterating products with customers. This individual will build and lead a team to develop and commercialize the product by building on the full breadth of Mastercard’s technology stack, driving adoption of secure digital payments to new contexts, interfaces, and market segments.
- Own the strategy, roadmap, and commercialization of Mastercard's Agent Pay and Agent Pay for Machines, from market test through commercial launch and global scale.
- Act as product and business/strategy lead. Collaborate and interface daily with internal and external stakeholders and customers, including frontier AI labs, agentic commerce providers, large issuers, digital wallets, merchants, acquirers, and payment service providers, to ensure the strategic (competitive, commercial) landscape is thoroughly understood, documented, and incorporated into the product roadmap. Regularly present these findings to executive leadership.
- Define and evolve the Agentic ecosystem architecture, including participant roles, trust frameworks, credential models, program rules, and operating standards required to scale machine\-to\-machine and agent\-driven payments globally.
- Direct cross\-functional delivery of all workstreams spanning onboarding, architecture, , developer tooling, specifications and protocols, credential and registry services, settlement systems, dispute processes, production operations, and reporting.
- Build and maintain healthy partnerships across blockchain, engineering, franchise, legal, treasury, and regional teams to accelerate delivery of the product vision.
- Help to develop and refine product value propositions that resonate with customers, balance contrasting interests among customer sets, and function across target markets / regions with different consumer\-level considerations.
- Drive primary inputs for the commercial strategy for the products, including pricing at the global and regional level, ongoing P\&L, and long\-term revenue projections.
- Serve as one of the primary internal and external voices representing the product in customer and partner engagements.
- Help to define the strategy for development, pilot, go\-to\-market, and scaling of the products, continual assessment of product/market fit, and direction on product pivots.
- Plan \& track product performance and develop product enhancements to address emerging customer requirements or scale customer adoption.
- Support adjacent products and drive connections between products and features.
All About You
- You have a 10\+ year track record of strong product leadership, having driven cross\-functional teams through successful 0\-1 and 1\-n product journeys.
- Comfortable acting as a decision maker in high\-ambiguity, high\-pressure workstreams. You have a demonstrated ability to synthesize disorganized inputs and oppositional stakeholder interests into a cohesive strategic point of view. You are an alliance\-builder and alignment\-finder even in contentious situations.
- Strong experience and knowledge of the business drivers of B2B and consumer payments, including familiarity across multiple payment rails, including card networks, stablecoins / crypto rails, ACH, and real\-time payments. Possess a keen understanding of the tradeoffs between rails and flows for merchants, PSPs, and SME buyers.
- Awareness and understanding of Mastercard's key customers, partners, and external stakeholders in these areas, including Issuers, Payment Service Providers, Acquirers, Digital Wallet Operators, and Merchants.
- High degree of comfort and firsthand experience using agentic coding tools – you’d be excited to showcase a few personal or professional projects, and you can distinguish between high\-quality and low\-quality AI practices and products.
- Fluency in the tech sector, including deep technology foundations and thorough industry awareness.
- Fluency in the business side of the tech industry, including current movers in AI and blockchain.
- Comfortable rapidly absorbing large volumes of complex business and technical knowledge. You seek out daunting intellectual challenges as a pastime.
- Possess a blend of analytical capability, strategic thinking, and emotional intelligence and able to dive deep on all areas of the business to deliver creative solutions to unstructured problems.
- Extremely strong presentations, supported by strong written and verbal communication skills.
- Comfortable communicating strategy, competitive insights, and complex ideas to executive leadership.
- Willing to hear new ideas and objectively consider challengers in the interest of achieving the best possible customer outcomes.
- Bachelor's degree required.
\#AI2
Mastercard is a merit\-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable\[email protected] and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
- Abide by Mastercard’s security policies and practices;
- Ensure the confidentiality and integrity of the information being accessed;
- Report any suspected information security violation or breach, and
- Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.
In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary and may be eligible for an annual bonus or commissions depending on the role. The base salary offered may vary depending on multiple factors, including but not limited to location, job\-related knowledge, skills, and experience. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance); flexible spending account and health savings account; paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave); 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro\-rated based on date of hire; 10 annual paid U.S. observed holidays; 401k with a best\-in\-class company match; deferred compensation for eligible roles; fitness reimbursement or on\-site fitness facilities; eligibility for tuition reimbursement; and many more. Mastercard benefits for interns generally include: 56 hours of Paid Sick and Safe Time; jury duty leave; and on\-site fitness facilities in some locations.Pay Ranges
New York City, New York: $245,000 \- $391,000 USD
O'Fallon, Missouri: $204,000 \- $326,000 USD
San Francisco, California: $245,000 \- $391,000 USD
Salary Context
This $204K-$391K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 Mastercard, 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 in Demand for This Role
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. This role's midpoint ($297K) sits 38% above the category median. Disclosed range: $204K to $391K.
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.
Mastercard AI Hiring
Mastercard has 6 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Engineering Manager. Positions span Salt Lake City, UT, US, New York, NY, US, O'Fallon, MO, US. Compensation range: $115K - $391K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.