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Who We Are Looking For
We are looking for a team leader to lead AI Security and Data Protection Governance and Controls, reporting to the Global Head of Data Protection. You will set the enterprise strategy and accountability model for secure, resilient, and responsible AI and data protection capabilities, translating emerging risk into business-aligned policies, standards, controls, investment priorities, and measurable outcomes. You will lead a multidisciplinary team and partner with senior leaders across Security, Technology, Risk, Compliance, Legal, Procurement, Internal Audit, and the business to embed security-by-design and scalable governance across enterprise platforms, applications, data, and third-party services.
Why This Role is Important To Us
The team is part of Global Cybersecurity, a function vital to protecting the confidentiality, integrity, availability, and resilience of the firm's information and technology environment. This leadership role is accountable for anticipating AI, data, and emerging technology risks; establishing a consistent enterprise control posture; directing remediation and transformation priorities; and representing the program with executive leadership, clients, auditors, and regulators. The role will enable the organization to adopt AI and data-driven capabilities securely while maintaining regulatory readiness and sustainable risk reduction.
What You Will Be Responsible For
In this role, you will:
- Set the enterprise vision, strategy, governance model, and multi-year roadmap for AI security, data protection, and emerging technology risk.
- Lead, develop, and retain a multidisciplinary team responsible for governance, control oversight, risk assessment, assurance, program execution, and stakeholder engagement.
- Establish enterprise policies, standards, control objectives, decision rights, risk ownership, and accountability across AI models, data assets, applications, infrastructure, cloud, identity, networks, and third-party services.
- Direct the discovery, inventory, classification, and risk assessment of AI models, sensitive data, security controls, platforms, integrations, and critical technology dependencies.
- Approve risk-based prioritization criteria and investment decisions using data sensitivity, business criticality, external exposure, retention, control maturity, regulatory impact, and remediation complexity.
- Sponsor minimum control requirements, testing and evidence standards, exception governance, compensating controls, risk acceptance, and remediation expectations across the enterprise.
- Oversee the transition of vulnerable or noncompliant implementations to approved secure technologies and control solutions, including milestones, funding, dependencies, control gates, and executive escalation.
- Partner with Security Architecture and Engineering leaders to embed AI security, data protection, security-by-design, and adaptable control requirements into architectures, engineering standards, and reusable implementation patterns.
- Establish third-party AI security and data protection requirements for products, cloud services, strategic vendors, due diligence, contracts, and ongoing oversight.
- Serve as the senior subject-matter authority for AI security and data protection governance in engagements with executive leadership, Risk, Compliance, Legal, Internal Audit, external auditors, clients, and regulators.
- Define management reporting, key risk indicators, performance measures, and executive dashboards covering inventory, exposure, control compliance, exceptions, remediation, roadmap delivery, and residual risk.
- Own program operating rhythm, resource planning, budget priorities, and cross-functional governance to drive timely decisions and measurable risk reduction.
What We Value
These skills will help you succeed in this role:
- Enterprise leadership experience spanning AI security, data protection, security architecture, cybersecurity governance, technology risk, and control transformation.
- Ability to convert complex technical, regulatory, and emerging risks into clear enterprise strategy, policy, control requirements, funding priorities, and executive decisions.
- Strong judgment in control design, assurance, exception management, risk acceptance, issue remediation, and regulatory engagement within a large, regulated organization.
- Proven success leading senior teams and complex, cross-functional transformation programs with measurable outcomes, clear accountability, and sustained adoption.
- Executive presence and influence across Security, Engineering, Infrastructure, Cloud, Application Development, Data, Risk, Legal, Procurement, Audit, and business leadership.
- Data-driven, risk-based decision making with the ability to balance strategic direction, delivery urgency, regulatory obligations, and operational resilience.
- Commitment to developing talent, simplifying governance, standardizing controls, and building scalable operating models.
Education & Preferred Qualifications
- Degree required: Bachelor's degree in Information Security, Computer Science, Engineering, Mathematics, Data Science, or a related discipline; advanced degree preferred.
- Years of experience: 13+ years of progressive experience in cybersecurity, AI security, data protection, security architecture, technology risk, or control governance, including significant leadership responsibility in a large, complex organization.
- Leadership experience: Demonstrated experience leading managers and senior professionals, setting enterprise strategy, governing multi-year transformation, and influencing executive-level decisions.
- Certifications required or preferred: CISSP, CISM, CRISC, CCSP, cloud security, privacy, AI governance, or comparable industry certifications preferred.
- Knowledge of tools or technologies: AI model and data discovery; data classification and protection; PKI; certificate and key management; cloud and enterprise security architectures; asset, configuration, governance, risk, and compliance platforms; and executive reporting or dashboard technologies.
- Additional language requirements: N/A.
- Other qualifications: Experience establishing enterprise policies, standards, and controls; directing cross-functional transformation; governing third-party risk; managing executive and regulatory relationships; and communicating complex risk to technical and non-technical audiences.
Additional Requirements
Additional requirements include the ability to lead across global teams and time zones, support regulatory and audit engagements, manage sensitive information in accordance with company requirements, and provide executive coverage for critical program or risk matters. Travel may be required based on business needs.
Work Requirement
- Hybrid Work Requirement: Hybrid, subject to company and location-specific requirements.
- Shift Timings: Standard business hours with flexibility to support global stakeholders, executive engagements, and critical program, incident, audit, or regulatory needs.
Salary Range:
$120,000 - $217,500 Annual The range quoted above applies to the role in the primary location specified. If the candidate would ultimately work outside of the primary location above, the applicable range could differ.
Employees are eligible to participate in State Street’s comprehensive benefits program, which includes: our retirement savings plan (401K) with company match; insurance coverage including basic life, medical, dental, vision, long-term disability, and other optional additional coverages; paid-time off including vacation, sick leave, short term disability, and family care responsibilities; access to our Employee Assistance Program; incentive compensation including eligibility for annual performance-based awards (excluding certain sales roles subject to sales incentive plans); and, eligibility for certain tax advantaged savings plans.
For a full overview, visit https://hrportal.ehr.com/statestreet/Home .
About State Street
Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.
We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.
As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.
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Salary Context
This $120K-$217K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At State Street, 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 $212,000 based on 11,857 positions with disclosed compensation. This role's midpoint ($168K) sits 20% below the category median. Disclosed range: $120K to $217K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
State Street AI Hiring
State Street has 17 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Austin, TX, Boston, MA, US, Princeton, NJ. Compensation range: $157K - $217K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 tracked positions.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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