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About This Role
The Vice President, FinOps for AI will establish and lead the enterprise FinOps discipline for artificial intelligence. The role will provide financial governance, cost transparency, forecasting, allocation, optimization, and reporting across generative AI platforms, foundation models, AI gateways, agentic solutions, copilot technologies, accelerated compute, and embedded AI services. Acting as the strategic link among Technology, Engineering, Finance, Procurement, and business leadership, this leader will help ensure that AI innovation delivers measurable value with clear financial accountability.
Who We Are Looking For
Reporting to the Global Technology Services Cloud FinOps Operations Lead within the Technology Business Office, the candidate will define how Street governs, measures, forecasts, allocates, and optimizes AI spend across the enterprise.
This is a hands-on practitioner-leader role. The successful candidate will build the methodology, analyze cost and usage data, partner with engineering and application teams at the point of consumption, and present a clear view of AI economics to senior leadership. The role requires sound judgment and adaptability as taxonomies, tooling, ownership models, and vendor pricing continue to evolve.
Why This Role Is Important To Us
AI adoption and spending are growing rapidly and span cloud services, model providers, gateways, data platforms, accelerated compute, and software licensing. Because these services use pricing models that differ from traditional infrastructure, disciplined financial governance is essential to maintain transparency, manage risk, and guide investment decisions.
This role will create clear accountability for AI costs, establish unit economics before consumption patterns become entrenched, and ensure new workloads are estimated and sized before development begins. The work will support optimization targets and accelerate the shift from reactive reporting to cost-aware engineering and investment management.
What You Will Be Responsible For
- Establish the enterprise FinOps for AI discipline, including its scope, taxonomy, methodology, operating model, governance, controls, and performance measures.
- Create an integrated view of AI cost and consumption across hyperscaler services, accelerated compute, AI gateways, models, tokens, agents, copilots, data platforms, and embedded vendor capabilities.
- Define actionable unit economics—including cost per token, request, session, agent, and use case—and connect AI spending to products, services, business outcomes, and customer value.
- Lead budgeting, forecasting, scenario modeling, and variance analysis for AI investments, with early warning when consumption or run rate moves ahead of plan.
- Set allocation, showback, and chargeback standards, including tagging, attribution, shared-cost treatment, and reporting by business unit, application, product, and use case.
- Identify and deliver optimization opportunities through model selection and routing, prompt and context efficiency, caching, batching, inference right-sizing, accelerated-compute commitments, and retirement of low-value consumption.
- Track and validate realized savings and cost avoidance, clearly distinguishing actual results from estimates, forecasts, and recommendations.
- Embed cost estimation, financial guardrails, and onboarding guidance into AI intake and development workflows so teams understand expected run costs and trade-offs before they build.
- Partner with AI and cloud platform teams, engineering, architecture, security, procurement, finance, and business stakeholders to secure reliable data, influence consumption decisions, and support effective governance.
Define reporting and tooling requirements, validate implementation logic, and communicate AI spend, risks, trade-offs, and opportunities through executive showback, leadership reviews, and planning cycles.
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What We Value
These skills will help you succeed in this role:
- Practical FinOps or cloud financial management experience, with the credibility to influence engineering and business decisions tied to real consumption.
- Strong understanding of AI pricing and consumption drivers, including tokens, inference, context windows, model tiers, accelerated compute, licensing, and actual usage behavior.
- Ability to build a new operating discipline from the ground up by setting methodology, making defensible assumptions, documenting decisions, and adapting as the market matures.
- Strong analytical judgment and a disciplined distinction among actuals, forecasts, estimates, opportunities, and recommendations.
- Clear, concise communication of technical cost drivers and business trade-offs to engineers, finance partners, and senior executives.
A results-oriented mindset focused on measurable, validated outcomes, supported by high standards for documentation, governance, and auditability.
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Education & Preferred Qualifications
- Bachelor’s degree in Finance, Accounting, Economics, Computer Science, Engineering, Information Systems, or a related field preferred
- 10+ years of equivalent relevant experience
- Significant experience across FinOps, cloud financial management, technology finance, or cloud and AI engineering, preferably in a large, regulated enterprise.
- Demonstrated success managing or optimizing cloud spend at scale across AWS, Azure, or GCP; exposure to Databricks, Snowflake, OCI, or similar platforms is beneficial.
- Direct experience with AI or machine-learning economics, such as model and inference costs, GPU or accelerated compute, AI gateways, token-based pricing, or AI software licensing, strongly preferred.
- Experience with budgeting, forecasting, allocation, tagging, showback or chargeback, optimization, and cloud commitment management.
- Familiarity with the FinOps Framework, FOCUS cost data standards, or TBM taxonomy; FinOps or relevant cloud certification preferred.
Proficiency working with cost and usage data and reporting platforms such as Power BI, Microsoft Fabric, Tableau, or comparable cloud financial analytics tools.
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Additional Requirements
Success requires strong partnership across cloud and AI platform engineering, application teams, architecture, security, procurement, finance, and business leadership. The candidate must be effective in a matrixed environment where pricing models, tooling, data, and ownership are evolving, and where influence often matters more than direct authority. Limited travel may be required.
Salary Range:
$110,000 - $188,750 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 $110K-$188K 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 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 $212,000 based on 11,857 positions with disclosed compensation. This role's midpoint ($149K) sits 30% below the category median. Disclosed range: $110K to $188K.
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
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 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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