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About This Role
About Us
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.
At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.
Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.
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.
Job Description
Visa is searching for a Manager, AI Project Management - Global Marketing Services to drive program management across the AI Transformation program. This role will manage four concurrent workstreams, Workflow Redesign, Agent Deployment, Data Foundations, and Change Management, ensuring the AI Transformation team delivers on its commitments on time and at scale. The ideal candidate is a seasoned program manager who understands how AI initiatives differ from traditional technology projects and can 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 Manager will own the program's operating rhythm: tracking timelines, milestones, and dependencies across every workstream, maintaining the RAID log, driving status reporting, and leading cross-team coordination that keeps a complex, multi-workstream transformation program on schedule. As one key workstream, the role will oversee the build-out of the Marketing Services operational knowledge base, working alongside data experts to ensure content is inventoried, structured with consistent metadata tagging and taxonomy, and made AI-ready so it can be effectively leveraged by AI tools and workflows.
The role requires a leader who can drive accountability across multiple concurrent workstreams, build structured processes from ambiguity, and maintain the operational discipline that enables a fast-moving transformation team to deliver consistently. It demands comfort with the unique requirements of AI projects, including experimentation cycles, data dependencies, and evolving requirements.
Reporting to the Director, AI Product Management, Global Marketing Services, this leader will work closely with workstream leads, Visa Technology teams, subject matter experts across Marketing Services regional teams, and cross-functional partners across Finance, Legal, Privacy, Risk, and Compliance to ensure all AI Transformation initiatives are tracked, risks are surfaced, dependencies are managed, and delivery commitments are met.
Key Responsibilities
- Run program management for AI Transformation: Own timeline, milestone, and dependency tracking across all AI Transformation workstreams. Maintain the master program plan, identify critical path items, and proactively flag scheduling conflicts or resource constraints. Drive the program's operating rhythm including standups and milestone reviews. Support the Senior Director on steering committee preparation and stakeholder management.
- Maintain RAID log and delivery-risk escalation: Own the program's RAID log. Ensure risks are identified early, mitigation plans are in place, and escalations reach the right stakeholders. Proactively flag AI-specific program risks including data readiness delays, cross-workstream dependencies, and adoption blockers.
- Deliver status reporting and cross-team coordination: Produce status reports, dashboards, and executive updates on program health, milestone progress, and delivery risks. Coordinate across workstreams to maintain alignment and resolve blockers.
- Manage the Knowledge Management workstream: Oversee the build-out of the Marketing Services operational knowledge base. Work closely with global and regional Marketing Services peers and data experts to drive inventorying and collection of operational content, ensure consistent metadata tagging and taxonomy are applied, and make the repository AI-ready. Coordinate with subject matter experts to source content, track gaps, and drive sign-off.
- Drive cross-workstream dependency management: Identify and manage interdependencies between workstreams, ensuring that upstream deliverables land on time to unblock downstream work. Facilitate trade-off decisions when competing priorities arise.
- Support governance, compliance, and responsible AI alignment: Partner with Legal, Risk, Privacy, Compliance, and Technology teams to ensure AI-enabled offerings and pilots 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
- 8 or more years of work experience with a Bachelor's degree, or at least 6 years of work experience with an Advanced Degree, such as a Master's, MBA, or a PhD.
- Experience in program management, project management, or management consulting, including managing complex programs with multiple concurrent workstreams, dependencies, and cross-functional stakeholders.
- Familiarity with AI projects and their unique requirements, including data readiness, experimentation cycles, and responsible AI considerations.
- Understanding of how LLM-based solutions are built, including familiarity with the generative AI technology stack, prompt engineering, and the data and infrastructure requirements that underpin AI agent development.
- Hands-on experience with AI productivity tools such as Claude, ChatGPT, and similar platforms, with the ability to apply them to streamline program management workflows.
- Experience with RAID management, milestone tracking, critical path analysis, status reporting, and delivery-risk escalation.
- Experience with program and project management tools (e.g., Wrike, Jira Align, MS Project, or similar).
- Experience with agile methodologies, specifically Scrum and Kanban.
- Demonstrated success driving complex, cross-functional programs with measurable business impact in a global, matrixed organization.
- Comfort working with content structuring concepts such as metadata tagging, taxonomy design, and preparing content for AI ingestion. Deep expertise is not required, but practical familiarity and the ability to partner effectively with data experts is essential.
- Strong ability to translate complex program status, AI project dynamics, and delivery risks into clear executive communications and stakeholder updates.
Preferred Qualifications
- 10 or more years of work experience with a Bachelor's Degree, or at least 7 years of work experience with an Advanced Degree, such as a Master's, MBA, or a PhD.
- Demonstrated hands-on experience with a range of AI tools and platforms, with the ability to use AI to streamline operations and deliver measurable business impact.
- PMP, PgMP, or similar program management certification.
- 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.
- Familiarity with payments, banking, fintechs, or card products is a plus.
Additional Information
This role requires a leader who combines strategic 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 messages to senior-level executives. Familiarity with AI project 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 $136,000.00 to $ 217,000.00 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 $136K-$217K range is below the median 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 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($176K) sits 17% below the category median. Disclosed range: $136K to $217K.
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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