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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
Visa is seeking a Director, Product Strategy & Operations to help lead the growth and execution of Agentic Commerce within the Commercial & Money Movement Solutions organization.
This is a highly visible leadership role responsible for helping define and operationalize Visa's commercial agentic commerce strategy. The Director will serve as a key thought partner to executive leadership and product leadership, driving strategic planning, cross-functional execution, operating rigor, ecosystem engagement, and product strategy across numerous initiatives.
The successful candidate will operate at the intersection of product, operations, and commercialization. They will help shape investment priorities, influence roadmap decisions, drive enterprise alignment, and support execution of strategic initiatives from concept through deployment.
The ideal candidate combines strategic thinking with operational excellence, thrives in ambiguity, and has experience scaling emerging businesses in highly matrixed environments.
Key Responsibilities
Strategic Leadership & Portfolio Direction
- Help define where Visa should play across the agentic value chain, including not just the moment of payment but also upstream and downstream in B2B and money movement workflows.
- Identify and evaluate new growth opportunities, adjacencies, and business models that can expand Visa's role in agent-enabled commerce.
- Lead strategic assessments and recommendations regarding build, buy, partner, or invest decisions across emerging capabilities and ecosystem opportunities.
- Drive portfolio prioritization across multiple initiatives, ensuring investments align with commercial impact, customer demand, competitive positioning, and long-term strategic objectives.
- Evaluate market trends, competitive dynamics, regulatory movement, and tech innovation to inform GTM narrative and roadmap choices.
- Partner with CMS, GPP and Ventures leadership to support multi-year strategic plans, investment frameworks, and business cases for new products, platforms, and capabilities.
- Develop executive narratives, recommendations, and decision materials for senior leadership reviews and strategic planning discussions.
Product Investment
- Help identify new agentic commerce opportunities and support the product management team in the journey from concept to commercialization.
- Assess value-added service opportunities across the B2B value chain and in different use cases or verticals e.g., payment intelligence, supplier enablement, treasury workflows, trust infrastructure, and embedded financial services.
- Define success criteria and go-forward recommendations for pilot programs, prototypes, and emerging product concepts.
- Support the product management team in structured experimentation and learning agendas to validate customer demand, commercial viability, and strategic fit.
Portfolio Execution & Business Operations
- Establish operating mechanisms that provide visibility into portfolio priorities, progress, risks, dependencies, and investment decisions. Drive alignment across cross-functional teams such as Product, Engineering, Legal/Risk, Finance, and GTM teams to ensure smooth and fast execution.
- Partner closely with product leaders to ensure roadmaps reflect high-impact priorities.
- Lead annual planning, quarterly business reviews, resource allocation discussions, and portfolio performance management. Manage monitoring metrics and indicators of success.
- Serve as a central point of coordination across multi-team initiatives within Agentic Commerce.
Commercialization & Go-to-Market Alignment
- Partner closely with GTM and Partnerships leads to ensure overarching narrative is cohesive and delivery strategies are impactful. Translate product and platform strategy into clear value propositions, potential adoption strategies, and commercialization priorities.
- Flex into GTM responsibilities by supporting key client engagements, strategic account discussions, and executive briefings related to Agentic Commerce initiatives.
Thought Leadership, Evangelism & Education
- Serve as a leading internal advocate for Agentic Commerce across CMS and Visa.
- Develop frameworks, narratives, and educational content that help stakeholders understand the implications of agent-driven payments and money movement. Educate stakeholders on industry developments, competitive activity, and ecosystem shifts.
- Push on strategy development for longer horizon opportunities
- Represent Visa in industry engagements.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
This role must sit in San Francisco, CA.
Qualifications
Basic Qualifications:
- 10 or more years of work experience with a Bachelor’s Degree or at least 8 years of work experience with an Advanced Degree (e.g. Masters/ MBA/JD/MD) or at least 3 years of work experience with a PhD
Preferred Qualifications:
- 10+ years of experience in product strategy, business strategy, operations, product management, management consulting, corporate development, fintech, payments, or related fields.
- Experience in commercial payments, B2B payments, treasury, procurement, ERP ecosystems, accounts payable, accounts receivable, or money movement.
- Understanding of emerging AI technologies, autonomous agents, digital identity, trust frameworks, authentication, tokenization, or payment orchestration.
- Experience managing strategic partnerships and ecosystem development initiatives.
- Experience launching or scaling platform businesses, network businesses, or multi-sided marketplaces.
- MBA or equivalent combination of advanced education and relevant industry experience.
- Experience operating in zero-to-one innovation environments and driving transformation within large enterprises.
- Proven track record leading complex strategic initiatives in highly matrixed global organizations.
- Experience building new products, businesses, platforms, or ecosystems from concept through commercialization.
- Strong understanding of payments, financial services, fintech, software platforms, or digital commerce ecosystems.
- Demonstrated ability to influence executive stakeholders and drive outcomes without direct authority.
- Exceptional analytical, problem-solving, and structured-thinking skills.
- Outstanding communication skills, including executive presentations, board-level materials, business cases, and strategic narratives.
- Bachelor’s degree or equivalent.
Information for US Applicants
For roles located in the US, the estimated salary range for this position is $203,800 to $326,100 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 $203K-$326K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($264K) sits 25% above the category median. Disclosed range: $203K to $326K.
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.
Visa AI Hiring
Visa has 8 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, AI Product Manager. Positions span San Francisco, CA, US, Austin, TX, US, Foster City, CA, US. Compensation range: $191K - $326K.
Location Context
AI roles in San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% 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 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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