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Who We are looking for
We are seeking a strategic technology leader that can provide critical management, reporting, and support to the Chief Data and AI Officer organization.
The successful candidate will play a key role in supporting the execution of our operating plan in line with our strategic priorities, translating intent into action and ensuring alignment, operational discipline, and strategic follow\-through.
The role oversees a broad portfolio of cross\-functional capabilities including recurring responsibilities such as IT strategy and planning, governance, performance management, IT budget \& financial management, operating model design and communications. The role may also take on nonrecurring initiatives, such as time\-bound asks or ad hoc tasks that require senior oversight but fall outside established structures, these may include new strategic asks, interim leadership of emerging capabilities or one\-off projects that leadership want to keep close.
Why this role is important to us
COOs act as the operational right hand of the leadership team, ensuring rhythm, reliability and internal alignment within the IT organization. The ideal candidate will bring deep expertise in Artificial Intelligence, Enterprise Transformation, IT governance, financial oversight, and cross\-functional technical program delivery. Someone who excels at follow\-through and coordination, enabling the senior leadership team to operate at strategic altitude.
What you will be responsible for
- Partner closely with senior executives to develop \& plan critical strategic initiatives, responsible for governance and delivery of these programs.
- Financial Management: Accountable for financial planning across respective division, including the annual corporate budget process, monthly finance reviews, budget, mid\-year forecast, risks and opportunities.
- Coordinates expense saving initiatives, headcount, workforce planning and approval processes in alignment with central teams.
- Lead the performance management process that measures and evaluates progress against the divisions OKR / KPI’s.
- Responsible for governance and preparation for key Business Unit forums, including leadership calls, operating group meetings, leadership exchanges, and town halls.
- Partner management, supports creation of executive briefings with key strategic vendor partners bringing both technology and business together for co\-innovation and business opportunities.
What we Value
Advisory Skills and Leadership \- The candidate must have the ability to advise the executive management of the organization with a proactive approach. Candidates must have a proven ability to think and act globally, harmonizing global, regional and local objectives. Demonstrated ability to develop employees, manage and lead successfully in a matrix environment is important.
Execution Envoy \& IT Transformation Partner \- The successful candidate will focus on translating enterprise goals into delivery plans, ensuring alignment \& someone who can lead transformation efforts in operating model, delivery structure and workforce evolution.
Financial Transparency \- The candidate should be excited by the challenge of developing Total Cost of Ownership (TCO) and show back models that bring clarity and transparency to a large, complex IT cost base, clearly demonstrating how technology investment supports and enables each area of the business.
Strong Commitment to Data Integrity \- The candidate must be an advocate for data‑driven decision‑making, with a focus on maintaining a single source of truth and delivering trusted insights via integrated analytics platforms. Comfortable in analysing large and disparate datasets, slicing and interpreting data in multiple ways to produce clear, relevant insights.
Regulatory/Compliance Knowledge \- Adequate knowledge of the relevant legal and regulatory environment for the industry globally.
Influencing and Partnering Skills \- It is key that the individual has the proven ability to form constructive relationships with executive management and be recognized as a partner and trusted resource.
Education and Preferred Qualifications
- A minimum of 8\+ years' experience within an operations \& technology role with a proven track record in strategy and planning
- The ability to lead and influence in a global organization with a focus on building relationships and trust, managing delivery to multiple senior stakeholders. Ability to handle multiple priorities and lead teams either through influence or direct accountability
- Strong organizational, business analysis and program management skills with the ability to think strategically, adapt and manage ambiguity and complexity.
- Exceptional communication skills and relationship management capabilities, able to communicate with all levels of the organization effectively and across Operations and Technology subject matter experts.
- Willingness to jump into challenging situations or problems and drive to solutions
- Technical \& Financial acumen with experience overseeing multi\-million\-dollar portfolios in complex technology environment.
- Experience with Artificial Intelligence, Agentic AI, Data Management, Emerging Technologies, technology modernization, and cross\-functional program delivery highly desirable
- A relevant graduate degree or master’s qualification that can be leveraged in this role
Salary Range:
$170,000 \- $282,500 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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It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Salary Context
This $170K-$282K 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 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 $214,900 based on 6,420 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($226K) sits 5% above the category median. Disclosed range: $170K to $282K.
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.
State Street AI Hiring
State Street has 13 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Quincy, MA, US, Boston, MA, US, Cambridge, MA, US. Compensation range: $157K - $282K.
Location Context
AI roles in Boston pay a median of $210,000 across 166 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 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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