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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
Overview:
The Global Data Office:
As artificial intelligence and data-driven technologies become increasingly central to Visa’s innovation agenda, the Global Data Office (GDO) is expanding its AI and Data Governance function to ensure responsible, compliant, and effective use of these capabilities across the enterprise.
In this role, you will drive Visa’s Responsible Data Use program – evaluating cutting-edge AI and data initiatives – to safeguard trust and unlock business value. You will be at the forefront of Visa’s innovation, guiding how new AI solutions roll out globally while ensuring they meet our obligations to clients, regulators, and consumers. This position offers a unique opportunity to lead cross-functional collaborations, take ownership of mission-critical processes, and shape policies that govern data and AI use at Visa.
Key Responsibilities *(*What You’ll Do):
- Drive and enhance the Data & AI Use Case Assessment process – a key engine of Visa’s innovation – enabling calculated, transparent risk-taking for faster business growth. You will continuously improve this process to support new AI/data initiatives while maintaining compliance and trust.
- Execute a wide range of initiatives to implement Visa’s Responsible Data Use framework, including spearheading organizational change management efforts that embed data ethics into the company’s culture.
- Collaborate across all Visa regions and functions as a catalyst in Visa’s innovation pipeline. Work cross-functionally with teams (Product, Innovation, Analytics, Privacy, etc.) to operationalize data use guidelines globally, ensuring every project meets our standards and obligations.
- Conduct thorough Data & AI use case reviews: Examine incoming data use requests in detail and evaluate their risk against established Data & AI policies and rules. Where necessary, dive deep into technical specifics (even reverse-engineering algorithms) to ensure compliance and effectiveness.
- Lead end-to-end analysis of use cases: Take charge of reviews for complex AI and data projects. Identify and document potential risks with precision, bring in additional subject-matter experts for insight when needed, and synthesize all feedback into clear, actionable review reports.
- Take ownership of expert consultations: Engage internal subject-matter experts (legal, compliance, technology, etc.) to gather feedback on data use scenarios that fall outside existing guidelines. You’ll own these collaborations, ensuring that no critical insight is missed when assessing novel or ambiguous cases.
- Drive policy improvements: Proactively identify gaps or gray areas in current data use policies. Coordinate with internal stakeholders to propose well-founded policy updates or alternative solutions, backing your recommendations with solid facts and analysis to drive leadership discussions.
- Champion training and awareness: Help lead workshops and training sessions that educate Visa staff on our Data Use Policy and core data values. Be prepared to step up and lead training sessions, inspiring teams across the organization to uphold responsible data practices.
- Amplify our Data & AI Policy & Governance message: Create and curate engaging content for the Global Data Office’s internal sites and our external “Data @ Visa” pages. Your content will showcase progress, highlight success stories, and reinforce the importance of responsible AI and data use.
- Communicate progress and insights: Prepare compelling materials (presentations, reports, dashboards) to socialize Data & AI Use Policy developments and share insights from Data Use Case Assessment reviews with stakeholders at all levels. Ensure transparency, buy-in, and alignment on data governance initiatives.
- Take on new challenges: Support ad-hoc Data Policy projects and new initiatives as they arise. Whether it’s a sudden regulatory change or a novel business proposal, you are ready to jump in, adapt, and contribute to solutions in real time.
- Connect policy and practice: Make sure learnings from Data & AI use case reviews inform the broader data governance strategy. Share insights across teams (“cross-pollination”) so that our policies, guidelines, and operations all advance together in a unified, informed way.
- Partner with data stakeholders: Work closely with Data Stewards and data requestors to gather complete and detailed information for each use case upfront. By ensuring you have all the facts, you enable more efficient reviews and more effective risk assessments.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications:
- 5 or more years of relevant work experience with a Bachelors Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD
Preferred Qualifications:
- 6 or more years of work experience with a Bachelors Degree or 4 or more years of relevant experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or up to 3 years of relevant experience with a PhD
- Proven experience in data and AI domains: Solid background in data science, data governance, data privacy, or a related field. (Experience in the payments or fintech industry is a plus.)
- Deep understanding of AI/ML technologies: Familiarity with cutting-edge techniques – particularly generative AI and large language models (LLMs) – and a strong awareness of ethical AI and responsible data use frameworks.
- Technical proficiency: Hands-on skills in quantitative analysis and data tooling (e.g. statistics, coding, or data engineering). While not expected to apply these skills directly, you should be comfortable interpreting complex datasets and algorithms to evaluate complex datasets or algorithms, and you grasp how to apply mathematics and statistics in assessing policy compliance.
- Knowledge of data regulations: Familiarity with evolving data and AI legislation (e.g., EU AI Act, GDPR, CCPA) and the ability to incorporate regulatory requirements into your assessments and recommendations.
- Exceptional problem-solving abilities: Creative, out-of-the-box thinking to tackle complex data challenges. You find solutions that satisfy policy objectives while enabling business innovation.
- Track record of results: A history of delivering high-impact projects in fast-paced, high-volume environments. You meet ambitious goals and can handle multiple priorities without sacrificing quality.
- Strong communication & stakeholder management: Excellent interpersonal skills with a proactive communication style. You’re able to engage and influence cross-functional teams – from engineers to lawyers to business leaders – ensuring everyone understands and aligns with data policy objectives.
- Clarity in complexity: Ability to interpret complex technical documents (policies, standards, procedures) and distill them into clear, concise guidance for non-technical audiences. You can translate “legal/technical speak” into actionable business terms that others readily grasp.
Soft Skills & Work Style (What Makes You Successful in This Role):
- Thrives under pressure: Energized by challenges, you excel in high-pressure, high-volume settings – delivering quality results even when working with demanding stakeholders and tight deadlines.
- Team leadership and collaboration: A true team player who is highly collaborative and diplomatic. You build strong relationships across diverse teams and lead by example, fostering an environment of trust and high performance.
- Meticulous and quality-focused: Keep attention to detail in everything you do, while still keeping sight of the bigger picture. You take pride in producing work that is accurate and of high quality.
- Exceptional problem solver: Superior analytical thinking with demonstrated intellectual rigor. When faced with a complex or ambiguous problem, you approach it systematically and resourcefully, finding innovative solutions.
- Self-motivated with ownership mentality: An independent self-starter who takes full ownership of your responsibilities. You have a strong sense of accountability for your deliverables and drive initiatives forward with minimal oversight.
- Adaptable and flexible: Comfortable working in a fast-changing, often ambiguous environment. You adapt quickly to new information and changing circumstances, staying effective and positive amid uncertainty.
- Open and innovative mindset: Open-minded and receptive to feedback and new ideas. You actively listen to others’ perspectives and are willing to change approach when current processes aren’t sufficient, always seeking better solutions.
- Enthusiastic & results-driven: Bring a high level of enthusiasm to your work and maintain an unrelenting focus on results. You’re driven to exceed targets and set new standards, motivated by the impact you create.
- Global mindset: Excited to work in a multicultural, global environment. You value diverse perspectives and are respectful and mindful of cultural differences when collaborating with colleagues around the world.
Information for US Applicants
For roles located in the US, the estimated salary range for this position is $156,800.00 to $ 251,100.00 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.
Salary Context
This $156K-$251K range is above 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. Disclosed range: $156K to $251K.
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
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 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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