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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 Cyber Analytics and AI Innovation organization builds the security capabilities that let the enterprise adopt Generative AI responsibly and safely. This is an innovation-oriented team: our charter evolves as GenAI threats evolve, and our core service is a growing suite of guardrail services that protects AI applications, LLMs, agents, tools, and data interactions from AI-specific threats.
As a Senior Software Engineer, GenAI Security, you'll be a hands-on individual contributor who owns problems end-to-end — from technical design and prototyping through production deployment, monitoring, and support. Because this is an innovation org, you won't be boxed into one feature lane: your work may span detection engines, agent and tool security, evaluation frameworks, or platform integration as priorities shift, and you'll often be handed a problem with real latitude in how you solve it rather than a fixed spec.
Essential Functions
- Build and operate GenAI security systems: design, build, deploy, and maintain production services and APIs that detect and mitigate risks such as prompt injection, jailbreak attempts, sensitive-data exposure, and unauthorized or unexpected tool/agent behavior — spanning LLMs, agents, tools, APIs, MCP servers, plugins, and enterprise data sources. Apply deterministic, policy-driven controls where predictable enforcement matters, and model-based approaches where judgment is needed.
- Measure and improve: build automated evaluation, testing, and feedback loops, plus production observability (logging, metrics, tracing, alerting, dashboards), so detection quality, latency, and false-positive/negative rates stay visible and auditable — then use that telemetry to close gaps.
- Integrate and scale: containerize and deploy services with Docker and Kubernetes, and integrate GenAI security capabilities into enterprise applications, model gateways, developer workflows, and cloud or on-premises platforms.
- Practice secure engineering: apply threat modeling, secure coding, dependency and secrets management, code review, and vulnerability remediation; participate in architecture and design reviews and document decisions, interfaces, and operational requirements clearly.
- Explore and influence: evaluate emerging GenAI security technologies and frameworks through structured proofs of concept, provide technical guidance to other engineers, and help shape the team's engineering standards and practices.
- Collaborate and operate: work with engineers, researchers, architects, product managers, and infrastructure teams to translate emerging AI-security risks into product capabilities, and support production releases, incident analysis, and defect resolution.
Position Location
This position may be based in Ashburn, Virginia, under Visa's hybrid working model. The expectation for days in the office will be confirmed by the hiring manager.
No relocation budget is available for this position.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications
- 2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience
- Professional software-development experience building backend services, APIs, platforms, or distributed applications.
- Experience developing production software using Python, Java, Go, or another modern programming language.
- Experience with cybersecurity, secure software development, application security, cloud security, AI security, or a closely related discipline.
Preferred Qualifications
- Advanced degree (M.S. or Ph.D.) in computer science, applied math, or a related field.
- Strong proficiency in Python and at least one additional language such as Java, Go, or Rust, with solid grounding in distributed-systems fundamentals — software architecture, design patterns, data structures, concurrency, API design, and performance optimization — applied to highly available RESTful, asynchronous, or event-driven services.
- Hands-on experience with Generative AI application architectures, including LLM APIs, embeddings, agentic workflows, tool calling, model gateways, and orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or LlamaIndex, along with familiarity with MCP, AI-agent tools, plug-ins, or similar emerging integration patterns.
- Understanding of GenAI-specific security risks (prompt injection, jailbreaking, sensitive-data exposure, insecure output handling, unauthorized tool use, data exfiltration) and hands-on experience implementing controls such as input validation, output filtering, policy enforcement, access control, audit logging, rate limiting, and secrets management.
- Knowledge of secure software-development lifecycle practices — threat modeling, vulnerability management, and application-security principles — applied in day-to-day engineering.
- Experience developing evaluation frameworks or test suites for LLM applications, including adversarial or red-team testing, regression testing, and false-positive/false-negative analysis.
- Experience with cloud-native deployment and operations — Docker, Kubernetes, CI/CD, infrastructure automation, and observability platforms (such as Splunk, Elasticsearch, or Grafana) for metrics, logging, tracing, alerting, and production troubleshooting.
- Familiarity with GPU-backed inference, CUDA, NVIDIA technologies, or model-serving frameworks is beneficial but not required.
- Ability to evaluate new technologies objectively against security, quality, latency, scalability, maintainability, and operational-readiness criteria, and to work independently on ambiguous, complex assignments.
- Clear written and verbal communication skills, including the ability to explain technical and security tradeoffs to different audiences, and experience collaborating effectively in Agile, cross-functional environments spanning engineering, cybersecurity, infrastructure, research, and product.
Information for US Applicants
For roles located in the US, the estimated salary range for this position is $118,700.00 to $ 183,600.00 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 $118K-$183K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($151K) sits 29% below the category median. Disclosed range: $118K to $183K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Visa AI Hiring
Visa has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer, Data Scientist. Positions span San Francisco, CA, US, Foster City, CA, US, Ashburn, VA, US. Compensation range: $171K - $326K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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