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About This Role
Location: San Francisco Other locations: Anywhere in Region Salary: Competitive Date: Oct 7, 2026 Job description
Requisition ID: 1750091 Location: San Francisco, Palo Alto, San Jose, San Mateo
At EY, we’re all in to shape your future with confidence.
We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.
The Opportunity
Artificial Intelligence (AI) is rapidly transforming how organizations operate, and with that transformation comes an entirely new frontier of security, governance, and risk management challenges. Organizations are grappling with securing AI pipelines, managing model-level risks, operationalizing AI governance programs, and navigating an evolving regulatory landscape specific to AI technologies. As a Manager within EY's Cyber practice focused on AI Security, you will lead client engagements that span AI risk assessments, AI Security Posture Management (AI-SPM), AI governance program design, and secure AI architecture. This role offers the opportunity to drive meaningful client outcomes, lead high-performing teams, and help organizations navigate one of the most complex and consequential frontiers in cybersecurity—while building a differentiated practice at the intersection of AI and security.
Your Key Responsibilities
As a Manager on the AI Security team, you will lead and manage AI security and AI governance engagements across a range of industries. You will oversee AI security assessments, direct AI-SPM implementations, and guide clients through the design of governance frameworks, policies, and controls that enable responsible and secure AI deployment. You will work closely with client leadership—including CISOs, CIOs, and AI program owners—to evaluate AI risk postures, facilitate workshops and architecture reviews, and translate technical findings into prioritized, executive-ready recommendations. You will provide hands-on direction in the configuration and integration of AI security tooling, including platforms and harnesses, and embed security within clients' AI development and deployment pipelines.
As a Manager, you will be responsible for managing engagement workstreams, leading and mentoring teams of Senior Consultants and Consultants, and delivering high-quality client service within established timelines and budgets. You will monitor the rapidly evolving AI threat landscape, regulatory developments—including the EU AI Act and NIST AI RMF—and emerging AI security tooling, translating insights into actionable guidance for clients and EY's practice. In addition, you will contribute to business development activities, including proposal development, client presentations, thought leadership and account growth initiatives, and collaborate with professionals across EY's Cybersecurity and AI capabilities to deliver integrated solutions.
Skills and Attributes for Success
- Deep knowledge of AI/ML systems, architectures, and deployment patterns—including LLMs, generative AI, and MLOps pipelines—and the security risks they introduce.
- Ability to work closely with Senior Managers, Partners, and client executives to define engagement scope, manage expectations, and deliver successful outcomes.
- Experience advising organizations on AI security posture, AI governance program design, and responsible AI risk management.
- Hands-on familiarity with AI-SPM platforms and AI security tooling, including DevSecOps platforms and harnesses, with the ability to guide technical implementation teams.
- Ability to deliver high-quality work products within budget and established deadlines.
- Strong analytical, problem-solving, and critical-thinking skills applied to complex AI security and governance challenges.
- Ability to communicate technical AI security concepts clearly to both engineering teams and executive audiences.
- Strong presentation, facilitation, and stakeholder management skills.
- Experience leading and developing teams in a collaborative, fast-paced consulting environment.
- Commitment to quality, continuous learning, and staying current with the rapidly evolving AI security landscape.
To Qualify for the Role, You Must Have:
- A bachelor's degree in Cybersecurity, Computer Science, Information Systems, Engineering, or a related field, and approximately 5+ years of relevant experience, including at least 1–2 years in AI or machine learning security; or a graduate degree and approximately 4+ years of relevant experience.
- Experience in one or more of the following areas:
- AI security assessments, AI-SPM, or AI model risk evaluations
- AI/ML pipeline security, model security, and MLOps security practices
- AI governance, responsible AI policy development, and AI risk management frameworks
- DevSecOps and CI/CD security, including experience with platforms and harnesses
- Cloud security with experience securing AI workloads on platforms such as AWS, Azure, or GCP
- AI regulatory compliance and emerging AI-specific standards and guidance
- Cybersecurity architecture, risk management, and security controls design
- Familiarity with AI security frameworks and guidance, including NIST AI RMF, OWASP Top 10 for LLMs, and MITRE ATLAS.
- Working knowledge of foundational cybersecurity frameworks—including NIST CSF, NIST 800-53, and ISO 27001/27002—as applied within AI-enabled environments.
- Experience supporting or leading client-facing cybersecurity or AI security engagements.
- Strong written, verbal, and presentation skills.
- Willingness to travel based on client and business needs; travel estimated at 25–50%.
Ideally, You'll Also Have
- Professional certifications in cybersecurity (e.g., CISSP, CCSP, CISM, CRISC) or emerging AI/ML security credentials.
- Hands-on experience with AI-SPM platforms, MLOps tooling, and AI security automation technologies.
- Experience with CI/CD orchestration and integrated security tooling, including Harness, to design and enforce automated security controls within AI development pipelines.
- Knowledge of adversarial machine learning techniques, model explainability requirements, and AI supply chain risk management.
- Exposure to AI-related regulatory developments, including the EU AI Act, NIST AI RMF, and sector-specific AI guidance in financial services, healthcare, or critical infrastructure.
- Experience facilitating executive workshops, client steering committees, and AI security architecture reviews.
- Background in software development, data engineering, or ML engineering that informs deep technical AI security credibility.
- Consulting experience within a professional services environment, with a track record of managing client relationships and engagement delivery.
What We Look For
We seek professionals who combine deep AI security expertise with strong consulting and leadership capabilities. The ideal candidate can build trusted relationships with client executives, direct technically sophisticated teams, and deliver practical, well-grounded recommendations that help organizations govern and secure AI responsibly. Successful candidates bring the hands-on technical credibility to engage meaningfully with engineering teams alongside the communication skills and business acumen to advise at the executive level—and the drive to help EY build a market-leading AI security practice.
What we offer you
At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.
- The salary range for this job is: + New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $173,900 to $290,100 + Bay Area California offices – $181,200 to $302,100 + All other offices locations in the US, including Sacramento – $144,900 to $265,800
- Individual salaries within these ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
Are you ready to shape your future with confidence? Apply today.
- To make the most of your application experience, please limit yourself to two applications within a six-month period.
- EY accepts applications for this position on an on-going basis.
- For those living in California, please click here for additional information.
- At EY, our values set the foundation for how we work and the behaviors we expect of our people. Any misrepresentation or falsification of information or lack of integrity at any point in the recruiting process may result in withdrawal of your candidacy, revocation of an offer or immediate termination of employment.
EY | Building a better working world
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.
Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.
All in to shape the future with confidence.
EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law.
EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1-800-EY-HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY’s Talent Shared Services Team (TSS) or email the TSS at [email protected].
Salary Context
This $144K-$290K range is above 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 EY, 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. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $144K to $290K.
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.
EY AI Hiring
EY has 70 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Architect, AI Safety. Positions span Stamford, CT, Hoboken, NJ, New Brunswick, NJ. Compensation range: $142K - $374K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 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 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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