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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 of Product to lead a critical domain within Agentic Commerce for the CMS business unit. This role will focus on ecosystem enablement – ensuring issuers, suppliers, enablers, and technology partners can participate in agentic payments in a seamless, scalable, and secure way.
This leader will define how the trust layer evolves for agentic commerce, including agent identity, business and user identity, permissions frameworks, observability, and liability structures across complex, multi\-party transactions.
Key Responsibilities
- Define strategic approach and platform requirements for agent identity, business/user identity, authentication, and permissions in close partnership with Visa Intelligent Commerce and CMS product teams
- Define requirements for transaction\-level transparency and traceability across agent\-initiated payments, including audit logging, explainability, and compliance reporting
- Drive alignment with risk, compliance, fraud, and disputes teams to ensure controls meet evolving security and regulatory expectations
- Build or influence capabilities that provide CMS clients with appropriate levels of control, visibility, and confidence in agentic payment flows
- Define scalable integration patterns across ERP platforms, AP automation providers, issuer processors, and fintech partners
- Lead ecosystem pilots and partner integrations to validate and refine agentic payment models
- Partner with central Visa teams (e.g., Core Rules, Risk, Legal) to define liability frameworks and extend dispute/chargeback models to agent\-initiated transactions
Day\-to\-Day Responsibilities
- Lead cross\-functional working sessions with product, engineering, risk, and partner teams to drive alignment on requirements and priorities
- Translate ambiguous, emerging concepts in agentic commerce into clear product requirements and roadmap decisions
- Review and refine product artifacts (PRDs, architecture diagrams, API definitions) to ensure consistency with platform strategy
- Engage with internal sales and solutioning teams to ensure product direction aligns with real client needs and pipeline opportunities
- Participate in partner discussions with issuers, fintechs, and platforms to shape integration approaches and pilot design
- Track progress against roadmap milestones and unblock teams where dependencies or ambiguity exist
- Synthesize feedback from pilots, clients, and internal teams into actionable product improvements
- Communicate progress, trade\-offs, and key decisions to senior leadership in a clear and concise manner
Scope of Ownership
- Product Domain Ownership: End\-to\-end ownership of a core capability within the agentic commerce trust layer (e.g., identity, permissions, auditability, or liability frameworks)
- Global Reach: Solutions designed for global applicability across diverse regulatory and ecosystem environments
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
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
- 12 or more years of work experience with a Bachelor’s Degree or 8\-10 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 6\+ years of work experience with a PhD
- 10\+ years of product management experience, with a track record of leading complex platform or infrastructure products
- Strong background in payments, financial platforms, identity/authentication, or risk and controls systems
- Experience defining and scaling products that operate in multi\-party ecosystems (e.g., issuers, merchants, fintechs, processors)
- Proven ability to translate emerging technologies (e.g., AI, automation, APIs) into practical, scalable product capabilities
- Experience working with APIs, platform integrations, and developer\-facing products
- Strong understanding of enterprise workflows (e.g., ERP systems, AP/AR processes, procurement flows)
- Demonstrated ability to drive cross\-functional alignment across product, engineering, risk, legal, and go\-to\-market teams
- Excellent communication skills, with the ability to influence senior stakeholders and simplify complex topics
- Experience operating in ambiguous, fast\-evolving problem spaces and bringing structure to undefined areas
- Direct experience with AI/agent\-based systems, orchestration platforms, or automation tools (professional or personal)
- Familiarity with identity frameworks (e.g., KYC/KYB, authentication, authorization models, verifiable identity)
- Experience with auditability, compliance reporting, or financial controls in enterprise environments
- Exposure to dispute management, fraud prevention, or liability frameworks in payments
- Experience in launching new platform capabilities or ecosystem programs (e.g., partner integrations, developer platforms)
- MBA or advanced degree in a relevant field
U.S. Applicants Only
The estimated salary range for this position is $192,300 to $307,600 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 $192K-$307K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($249K) sits 14% above the category median. Disclosed range: $192K to $307K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Visa AI Hiring
Visa has 15 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, MLOps Engineer, Data Engineer. Positions span Foster City, CA, US, Austin, TX, US, Highlands Ranch, CO, US. Compensation range: $163K - $451K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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