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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.
Visa will accept applications for this role until at least 08\-20\-2026Job Description
Team background: The North America BizOps team is the engine behind the North America Region \& Regional President's agenda. We have a broad and exciting mandate: running the day\-to\-day management of the North America business, the Regional President's Office and Chief of Staff work, AI transformation, employee engagement, client events, and priority initiatives. If it matters to how North America runs — or where it's headed — we're usually the ones making sure it happens.
Position Details
We are building the team that will shape how North America adopts and scales AI, working across the business and AI initiatives to turn AI's potential into real world results. We are looking for an ambitious, entrepreneurial leader to help define the vision, operating model, success metrics, and engagement model, then lead the work and the team.
This leader will set the strategy and priorities for North America's AI transformation, helping define how AI is adopted, scaled, and translated into business value across the region. They will oversee a pod of analysts embedded into business teams to demonstrate what an AI\-first team looks like in practice, identifying manual, repetitive work, deploying practical AI solutions, and creating measurable business value. By demonstrating AI's value through accessible, real team examples, they will build repeatable playbooks that can scale across North America, helping employees meaningfully adopt AI and preparing the organization for broader transformation over time.
This leader will also serve as the connective tissue across North America's internal AI efforts, bringing together leaders, initiatives, and tooling across the business to scale successful solutions and ways of working. Success requires equal parts strategic thinking, hands\-on execution, people leadership, and the ability to create influence and momentum in a rapidly evolving space.
Key Responsibilities:
- Stand up and lead North America's internal AI transformation effort, building the strategy, prioritization framework, governance, and operating model needed to focus the region on the highest\-impact opportunities and scale adoption across the business.
- Lead North America's Embedded AI Pod, setting priorities and guiding the development of AI\-enabled solutions that eliminate manual, repetitive work and materially improve how teams operate.
- Coach and mentor Embedded AI Analysts, helping high potential early\-career technical and business resources grow into strong builders, problem\-solvers, and operators
- Redefine and lead the mission, operating model, and success measures for North America's AI Champions network and broader enablement strategy, creating scalable, self\-sustaining mechanisms for AI adoption through business\-led training, shared resources, practical playbooks, and knowledge sharing
- Act as the principal AI advisor to the North America Leadership Team, educating leaders on AI capabilities and helping determine where AI can most materially improve their teams' ways of working
- Serve as North America's internal lead for AI, maintaining visibility into AI initiatives across Visa, determining where North America should engage, partner, or influence, and driving collaboration in efforts with the greatest potential for business impact.
- Establish executive reporting and performance measurement processes to track adoption, business outcomes, usage trends, and the effectiveness of North America's AI transformation efforts
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 relevant work experience with a Bachelor’s degree or at least 8 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD
Preferred Qualifications:
- 10\-12 or more years of relevant work experience with a Bachelor’s Degree or 9 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD
- Experience leading AI\-enabled workflow transformation and managing a high\-performing team is required.
- Experience managing high\-potential, early\-career talent, including both technical and non\-technical team members.
- AI\-native. You use AI tools as a core part of how you work today. Bonus if you've led an AI transformation — spotting the right workflows, defining what success looks like, and driving the change management to make it stick. Strong working knowledge of enterprise AI platforms, foundation models, and agent orchestration frameworks, including Microsoft AI solutions (e.g., Microsoft 365 Copilot, Copilot Studio), leading model ecosystems (e.g., Claude, Grok, OpenAI, Gemini), and agentic tooling/platforms (e.g., Claude Cowork, OpenClaw).
- Builds from ambiguity. Excels at creating structure, strategy, and execution plans where no clear path exists, bringing clarity and momentum to complex, evolving problems.
- Sharp analytical and communication instincts. You can take messy, complex inputs, find the insight, and turn it into a clear, concise, executive\-ready output — written or verbal.
- Ramps fast. You get up to speed on new topics and teams quickly, add value early, and don't burden others to do it. You can size up an ambiguous situation, weigh the tradeoffs, and move.
- Business sense. You understand how Visa's business and clients work — or you know how to figure it out fast when you don't.
- Calm under pressure. Composure, judgment, and attention to detail hold up when the stakes are high and the clock is ticking.
- High EQ and strong people skills. You build trust quickly across functions and levels, and have well\-honed influencing skills.
- Executive presence. You're comfortable in front of senior executives — clear, concise, and credible whether you're presenting, pushing back, or thinking on your feet. Able to explain technical concepts clearly to non\-technical audiences.
- Low ego, high impact. You act with the end outcome and the stakeholder experience in mind. You step in where needed, look for ways to remove friction rather than add it, and care more about the result than who gets credit.
- Discreet. You handle highly sensitive information with professionalism and confidentiality
- Prior experience in management consulting will be highly preferred
Information for US Applicants
For roles located in the US, the estimated salary range for this position is $207,500\.00 to $ 332,400\.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 $207K-$332K 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 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($269K) sits 26% above the category median. Disclosed range: $207K to $332K.
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.
Visa AI Hiring
Visa has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Foster City, CA, US, San Francisco, CA, US, Bellevue, WA, US. Compensation range: $136K - $332K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 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
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