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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
Job Summary:
The Senior Director, AI Adoption \& Change Management, will lead the adoption, reinforcement, incentives, and measurement agenda required for VCA to become an AI\-first organization. This leader will own the adoption mechanisms, incentives, recognition, performance integration, and measurement framework needed to move VCA from AI awareness to sustained behavior change and measurable business impact.
The role will partner closely with VCA global leadership, regional leadership teams, practice leaders, HR, AI platform/product teams, workflow owners, and change champions across regions to ensure the AI transformation is adopted, reinforced, measured, and embedded into how VCA works every day. This is a senior transformation leadership role focused on building organizational muscle for AI\-first behaviors: helping teams adopt new AI\-enabled ways of working, reinforcing adoption across regions and practices, identifying barriers and feedback loops, and how progress will be measured and rewarded.
Key Responsibilities:
Adoption, Reinforcement \& Behavior Change:
- Own the adoption strategy for AI\-enabled ways of working across VCA, moving beyond communication and training into sustained behavior change.
- Design adoption mechanisms that make AI\-enabled behaviors visible, expected, supported, and rewarded — including champions networks, office hours, nudges, peer coaching, demos, recognition, and adoption campaigns.
- Partner with AI product/platform leaders and workflow owners to ensure new AI agents, set plays, and standardized workflows launch with clear use cases, training, feedback loops, and reinforcement mechanisms.
- Establish feedback loops across regions and teams to identify adoption barriers, usability issues, confidence gaps, and opportunities to refine agents, workflows, and enablement materials.
- Create and scale an AI champions network across regions and practices to accelerate peer\-led adoption and capture field learnings.
Incentives, Recognition \& Performance Integration:
- Partner with Talent, HR, and business leadership to align incentives, OKRs, recognition, and performance signals with AI\-first behaviors.
- Define what “good” AI\-enabled performance looks like by role, level, and workflow, including AI fluency, judgment, output validation, reuse, quality, productivity, and business impact.
- Support integration of AI expectations into competency models, role guidelines, manager calibration, promotion criteria, and hiring/interview guides.
- Design recognition approaches that showcase high\-impact use cases, practical wins, team adoption, reusable assets, and measurable improvements in delivery speed, quality, and client value.
Measurement, Governance \& Continuous Improvement:
- Define adoption and change KPIs, AI platform teams, Talent, and leadership, including usage, proficiency, workflow coverage, productivity, quality, business impact, and employee sentiment.
- Build governance routines to review adoption progress, unblock friction points, monitor change risks, and adjust rollout plans based on data and feedback.
- Create executive\-ready dashboards, updates, and recommendations that show progress against adoption, capability, engagement, and business impact goals.
- Ensure VCA’s change management approach evolves as AI capabilities, workflows, roles, and operating model decisions mature.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications:
- 12 or more years of work experience with a Bachelor’s Degree or at least 10 years of work experience with an Advanced degree (e.g. Masters/MBA /JD/MD), or a minimum of 5 years of work experience with a PhD.
Preferred Qualifications :
- 15 or more years of experience with a Bachelor’s Degree or 12 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, or MD), PhD with 9\+ years of experience
- 12–18\+ years of experience in change management, organizational transformation, management consulting, HR strategy, AI Transformation, workforce transformation, communications, or business transformation.
- Proven track record leading large\-scale transformation programs across complex, global, matrixed organizations.
- Strong experience designing and executing change strategies that include communications, training, stakeholder engagement, leadership enablement, adoption tracking, and behavior reinforcement.
- Experience supporting digital, AI, automation, analytics, operating model, or workforce transformation programs.
- Ability to translate strategic priorities into practical change plans, adoption mechanisms, learning journeys, and leader\-ready materials.
- Strong understanding of consulting delivery models, talent development, role evolution, capability building, and performance management is preferred.
Information for US Applicants
For roles located in the US, the estimated salary range for this position is $176,500 to $332,300 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 $176K-$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 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 $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 ($254K) sits 18% above the category median. Disclosed range: $176K 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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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