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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
Visa’s Technology Organization is a community of problem solvers and innovators reshaping the future of commerce. We operate the world’s most sophisticated processing networks capable of handling more than 65k secure transactions a second across 80M merchants, 15k Financial Institutions, and billions of everyday people. While working with us you’ll get to work on complex distributed systems and solve massive scale problems centered on new payment flows, business and data solutions, cyber security, and B2C platforms.
The Value\-Added Services Product Development (VAS PD) team drives the diversification of our revenue with products that differentiate Visa network and deliver valuable solutions across other networks. The Visa Engage, Resolve, \& Assist (VERA) team in VAS focuses on innovative, AI\-driven solutions for issuers, merchants and fintechs.
Bellevue is a key technology hub for Visa, driving innovation, generative AI, and platform modernization initiatives. As the organizational lead for this site, you will oversee diverse engineering teams, guiding them as they deliver results for their respective product partners. Your teams will participate in complex multi\-stakeholder initiatives where innovative integration patterns and emerging technologies are leveraged to enhance functionality and deliver greater value to the market.
The Opportunity:
- We are seeking a dynamic and innovative leader with a proven track record of delivering modern, large\-scale, and complex services and products powered by transformative technologies. The ideal candidate excels at leading through influence, inspiring high\-performing and diverse technical teams at all levels of the organization. You will drive to success by fostering strong collaboration with product teams to achieve ambitious business goals.
- In this role, you will provide hands\-on technical leadership for multiple development teams, focusing on defining, executing, and delivering both functional and non\-functional features on a scale. You will cultivate a culture of cross\-functional collaboration and engineering excellence, championing new ideas and introducing industry best practices to benefit both the team and the broader organization. This position requires balancing business needs and innovative solutions with operational excellence, all while adhering to regulatory, security, and privacy standards.
- You will be expected to rapidly evaluate and integrate new ideas and technologies from internal and external sources, aligning them with emerging business opportunities. By doing so, you will help build a culture of innovation and a relentless drive for excellence.
- We are looking for a proven technology innovation leader with exceptional communication and organizational skills to drive both immediate modernization and the definition of long\-term strategic solutions. Expertise in agile delivery, building purpose\-driven teams, and managing complex integration projects involving multiple internal applications and third\-party solutions is critical. Experience with Generative AI and Machine Learning is essential. Prior background in payments and AI is a strong plus.
- The successful candidate will thrive in the dynamic payments space and be comfortable leading global teams responsible for platform transformation. You will play a pivotal role in expanding our VAS solutions footprint, exploring new paths to revenue by building modular, open solutions that will scale with our business.
Essential Functions:
Innovation and Technology Leadership
- Spearhead the design, development, and implementation of innovative technologies and solutions, including generative AI and modern integration patterns.
- Foster a culture of innovation by encouraging experimentation, knowledge sharing, and the adoption of emerging technologies within the engineering team.
- Champion the design and development of APIs that integrate Value\-Added Services applications, platforms, and solutions to drive incremental business value.
Strategic Vision and Execution
- Develop and execute a strategic vision for the VAS platform that aligns with Visa's business goals and market opportunities.
- Identify new market opportunities and enhance existing products to support business growth and platform modernization.
- Lead the execution of the product modernization roadmap, ensuring scalability, reliability, and alignment with industry best practices.
Engineering and Operational Excellence
- Own the end\-to\-end software development lifecycle, ensuring timely delivery of quality, secure, and high\-performing solutions with significant business impact.
- Promote engineering excellence by driving best practices in quality, security, performance, scalability, and resilience.
- Manage technical debt, optimize development costs, and oversee the prioritization and delivery of enhancements and maintenance activities for multiple services.
- Drive automation in software development, testing, and deployment processes to enhance productivity and time\-to\-market.
- Ensure effective incident, change, and problem management within the platform.
Team Leadership and Talent Development
- Hire, retain, and develop high\-performing, diverse, global engineering teams.
- Invest in career development through mentoring, coaching, and fostering a culture of continuous learning and improvement.
- Build purpose\-driven teams with a strong focus on accountability, collaboration, and innovation.
Stakeholder and Client Engagement
- Collaborate cross\-functionally with geographically distributed development, product, operations, infrastructure, cybersecurity, and support teams to deliver complete solutions.
- Engage directly with clients and key stakeholders to understand needs, gather feedback, and ensure the VAS platform delivers exceptional value and innovation.
Operational Management
- Oversee daily operations including budgeting, planning, resource management, delivery tracking, quality assurance, partner relationships, and performance metrics.
- Ensure compliance with regulatory, security, and privacy standards across all development activities.
Continuous Improvement
- Continuously assess and improve technology stacks, development processes, and methodologies to enhance productivity, quality, and efficiency.
- Lead initiatives to modernize and scale the VAS platform, expanding market reach and client base through best\-in\-class technology solutions.
The Skills You Bring:
Energy and Experience: A growth mindset that is curious and passionate about technologies and enjoys challenging projects on a global scale
Challenge the Status Quo: Comfort in pushing the boundaries, ‘hacking’ beyond traditional solutions
Language Expertise: Expertise in one or more general development languages (e.g., Python, Java, NodeJS, C\#, C\+\+)
Builder: Experience building and deploying modern services and web applications with quality and scalability
Learner: Constant drive to learn new technologies such as Angular, React, Kubernetes, Docker, etc.
Partnership: Experience collaborating with Product, Test, Dev\-ops, and Agile/Scrum teams
\*\* We do not expect that any single candidate would fulfill all of these characteristics. For instance, we have exciting team members who are really focused on building scalable systems but didn’t work with payments technology or web applications before joining Visa.
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
- Advanced Degree (e.g. Masters or PhD) with 12\+ or more years of relevant experience.
- Hands\-on experience with emerging technologies, including Generative AI and Machine Learning.
- Demonstrated ability to manage multiple competing priorities in a fast\-paced environment.
- Experience building microservices using HTTP, REST, JSON, and XML.
- Demonstrated experience as an innovation leader, successfully guiding engineering teams to deliver new products or features to the market.
- Proven track record in managing software development teams delivering highly available, enterprise\-level, distributed applications using languages such as Java, Python, C\#, Golang, or similar.
- Deep understanding of Agile methodologies and software development lifecycle.
- Experience working in complex organizational environments, interpreting business and customer needs, and delivering optimal solutions.
- Ability to articulate technical and business issues and solutions to various internal and external stakeholders to support organizational objectives.
- Skilled in establishing technical goals for projects, defining actionable success metrics, and tracking progress.
- Strong experience in coordinating multiple product and technical teams, adept at removing roadblocks and collaboratively solving complex problems.
- Exceptional verbal, written, presentation, and facilitation skills, with the ability to communicate complex ideas clearly and concisely to diverse audiences.
- Experience delivering integrated solutions composed of both internally developed applications and third\-party commercial offerings.
- Proficient in measuring services from the user experience perspective, setting and monitoring SLAs and KPIs.
- Payment industry experience is highly desirable.
U.S. Applicants Only
The estimated salary range for this position is $232,300 to $451,800 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 $232K-$451K 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 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 $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 ($342K) sits 56% above the category median. Disclosed range: $232K to $451K.
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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