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Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.
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Job Description
About the Job
Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere, by being the best way to pay and be paid.
Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa.
Visa is searching for a Director, AI Product Management - Global Marketing Services to own the internal AI solutions and AI agent product portfolio for the Marketing Services organization. This role will identify, prioritize, and design AI-enabled opportunities that improve productivity, quality, speed, and scalability across Marketing Services. The ideal candidate is a seasoned product manager who understands how AI solutions are built, what data they require, and how to manage the iterative, experiment-driven nature of AI development.
This role will be part of the AI Transformation team within the Global Marketing Services organization. The Director will work closely with business stakeholders to translate business needs into product requirements, manage the internal use-case prioritization backlog, and serve as the business product owner within Technology sprint and build cycles. The role requires a leader who can bridge the gap between business stakeholders and technical teams, translating complex AI possibilities into actionable product roadmaps that deliver measurable value.
This leader will own the end-to-end internal AI product lifecycle, from opportunity identification and use-case prioritization through agent specification, MVP definition, build collaboration, and adoption measurement. The role demands comfort with ambiguity, strong stakeholder management skills, and the ability to drive alignment across business and technical teams on what to build, why, and in what order.
Reporting to the Senior Director, AI Transformation, Global Marketing Services, this leader will work closely with Data Scientists, Data Engineers, Technology partners, and business stakeholders across Marketing Services to ensure AI solutions and agents are designed around validated business needs, built on reliable data foundations, deployed through the organization's agent platform, and delivered through disciplined product management practices.
The Director will partner with peers on the Global Marketing Services AI Transformation team and across Marketing Services' regional teams, Finance, Technology, Legal, Privacy, Risk, and Compliance teams, and external vendors to ensure successful product design, development, deployment, measurement, governance, and adoption.
Key Responsibilities
- Own the internal AI product roadmap and use-case backlog: Prioritize AI-enabled opportunities across Marketing Services using a clear framework based on business impact, feasibility, and data readiness. Keep the roadmap current as priorities, technology, and capacity evolve.
- Co-lead workflow reimagination and future-state process design: Create or leverage industry frameworks to guide business stakeholders with redesigning future-state AI-enabled workflows. Partner with business subject matter experts to identify where AI solutions and agents improve quality, or accelerate delivery, and translate those into scoped product opportunities with clear success criteria.
- Define agent specifications and MVP scope: Author detailed agent and AI solution specifications covering scope, target users, data needs, integration requirements, guardrails, and MVP definitions. Partner with Data Scientists and Data Engineers to validate data availability, quality, and pipeline requirements.
- Translate business needs into product requirements and user stories: Develop product requirements, user stories, and acceptance criteria for Technology teams. Manage backlog refinement and grooming. Bridge communication between business and technical teams to resolve ambiguity and maintain alignment.
- Serve as business product owner in Technology sprint and build cycles: Participate in sprint planning, backlog refinement, demos, and retrospectives. Make prioritization decisions, provide timely feedback, and accept or reject deliverables against acceptance criteria.
- Collaborate with Data Scientists and Data Engineers on data needs: Partner with Data Science and Data Engineering teams to ensure data requirements for AI solutions and agents are identified early, validated for availability and quality, and incorporated into build plans.
- Co-lead adoption measurement and continuous improvement: Define and track product success metrics including adoption rates, user satisfaction, productivity impact, quality improvements, and business value delivered.
- Support governance, compliance, and responsible AI alignment: Partner with Legal, Risk, Privacy, Compliance, and Technology teams to ensure AI solutions and agents meet responsible AI, governance, regulatory, and Visa standards.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.Qualifications
Basic Qualifications
- 12 or more years of work experience with a Bachelor's degree, or at least 10 years of work experience with an Advanced Degree, such as a Master's, MBA, or a PhD.
- Experience in AI product management including owning a product backlog, writing requirements, and managing prioritization in an agile environment.
- Demonstrated mastery and hands-on experience with a range of AI tools and platforms, with the ability to use AI to drive innovation, streamline operations, and deliver measurable business impact.
- Experience defining and launching AI solutions, including AI agent specifications, intelligent automation, or AI-powered internal tools, from concept through production in partnership with Technology, Data Science and Data Engineering teams.
- Experience managing pilots and proof-of-concept programs, including soliciting user feedback, iterating on solutions, and scaling successful pilots into enterprise-wide solutions.
- Demonstrated success leading complex, cross-functional programs with measurable business impact in a global, matrixed organization.
- Strong ability to translate complex AI, data, and technology concepts into clear product requirements, executive-level recommendations, and stakeholder communications.
Preferred Qualifications
- 15 or more years of work experience with a Bachelor's Degree, or at least 12 years of work experience with an Advanced Degree, such as a Master's, MBA, or a PhD.
- Familiarity with data architecture, data pipelines, feature stores, and the data foundations required to support AI and machine learning solutions.
- Experience with workflow analysis, process redesign, and translating operational inefficiencies into product-led solutions.
- Familiarity with payments, banking, fintechs, or card products is a plus.
- Strong analytical, problem-solving, and strategic thinking skills, with the ability to operate in ambiguity, build structure in white space, and drive action across stakeholders and business units.
Additional Information
This role requires a leader who combines strategic product thinking with hands-on execution. The ideal candidate will have strong relationship-building skills, excellent written and verbal communication, personal presence, and the ability to clearly communicate compelling product vision and priorities to both technical and business audiences. Experience with AI product development dynamics, including iterative development, shifting requirements, data dependencies, and the need to balance speed with responsible deployment, is essential.
The candidate should be extremely organized, demonstrate quick follow-through, and bring a strong bias toward action. The role requires comfort working in fast-paced, matrixed environments and the ability to collaborate across regions and business units to execute strategy and drive measurable business outcomes.
Acceptable Position Location
Seeking a candidate who will be based in the Visa San Francisco Office in Mission Rock.
This is a hybrid position. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs. Visa is not offering relocation assistance for this role.
Information for US Applicants
For roles located in the US, the estimated salary range for this position is $179,000.00 to $ 287,000.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program.Work Hours
Varies upon the needs of the department.
Travel Requirements
This position requires travel 5-10% of the time.
Mental/Physical Requirements
This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers.
Visa is an EEO Employer
Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law, including the requirements of Article 49 of the San Francisco Police Code.
Salary Context
This $179K-$287K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Visa, 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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($233K) sits 10% above the category median. Disclosed range: $179K to $287K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Visa AI Hiring
Visa has 17 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer, Data Scientist. Positions span Austin, TX, US, San Francisco, CA, US, Foster City, CA, US. Compensation range: $171K - $326K.
Location Context
AI roles in San Francisco pay a median of $260,550 across 2,766 tracked positions. That's 22% above the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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