Interested in this AI/ML Engineer role at State Street?
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Role purpose: Serve as the senior Investments business owner for Artificial Intelligence (AI) — identifying where AI can improve investment outcomes, investment decision\-making, and workflows across research, portfolio management, trading, investment oversight, client portfolio management, and investment operations.
The role will help shape the future investment process by identifying how AI can become a sustainable competitive advantage across research, portfolio construction, risk management, trading, and client outcomes.
Location: Boston, Bangalore or London
Purpose: The Head of AI Capability and Adoption \- Investments will define and steward the use of AI across the investment lifecycle. This role partners with investment teams to identify where AI can enhance research, portfolio construction, trading, portfolio management, and client\-facing workflows.
Investment focus : This is not a generic enterprise AI, product management or transformation role. The successful candidate will bring strong investment domain knowledge and credibility to engage investment professionals in all front office functions on how AI can be applied responsibly to improve the investment workflows and business outcomes. This is a front\-office focused leadership role at the intersection of investing and AI. The successful candidate will work directly with CIOs, asset class leaders, portfolio managers, research analysts, and traders to identify where AI can improve investment decisions, generate insights, enhance risk management, and strengthen client outcomes.
How the role works: The role leads through an investment ecosystem of portfolio management, research, trading, operations, technology, data, and architecture partners. It frames priorities, trade\-offs, risks, value opportunities, and executive decisions while ensuring AI initiatives are grounded in real investment workflows and scalable delivery practices. The role will help embed a product\-oriented operating model for AI in Investments, ensuring that AI opportunities are managed across the lifecycle from problem definition and prioritization through delivery, adoption, measurement, and continuous improvement. This is a senior matrix leadership role. The role may have a small direct team over time, but will primarily deliver impact by mobilizing cross\-functional capacity across Investments, Technology, Data, Architecture, Risk, Governance, and Operations.
Organizational context: This role sits within the Investments organization and reports to the Head of Strategy \& Operations, Investments. Working closely with the Chief Investment Officer, CIOs, asset class leaders, portfolio managers, research leaders, and trading leaders, the role serves as a key bridge between investment priorities and the capabilities required to enable AI\-driven transformation across the investment platform. Over time, the role is expected to operate across the broader Ways of Working model, partnering across capability groups and acting at a portfolio level to align business priorities, product ownership, delivery capacity, governance, and adoption.
You will play a leading role in defining how AI is embedded across the investment platform, helping shape the future operating model for AI\-enabled investing.
As the Head of AI Capability and Adoption, Investments, you will:
Define the Investments’ AI strategy and roadmap
- Set the vision, priorities, and roadmap for AI across the investment lifecycle.
- Focus AI opportunities on investment decision quality, workflow efficiency, risk awareness, scalability and client outcomes.
Translate investment workflow needs into AI opportunities
- Work with investment teams to identify workflow challenges and identify opportunities for AI\-enabled improvements
- Ensure use cases solve real investment problems rather than technology\-led experiments.
Govern and prioritize the Investment AI portfolio
- Lead intake, prioritization, sequencing, and governance of Investments AI use\-cases.
- Applies product\-oriented use\-case management to frame use case trade\-offs based on investment value, risk, feasibility, reuse, adoption readiness, and lifecycle maturity.
Drive adoption in investment workflows
- Partner with investment teams to embed AI practices into day\-to\-day investment workflows.
- Drive adoption of AI\-enabled investment workflows and establish scalable practices that accelerate responsible use of AI across investment teams.
Lead alignment across the Investments AI ecosystem
- Bring together investment, technology, data, architecture, risk, and governance stakeholders around a shared AI roadmap and priorities.
- Facilitate decisions, resolve dependencies, and escalate issues requiring senior leadership attention.
Measure investment value, manage risk, and inform executive decisions
- Measurement adoption, workflow improvements, risk management, and business value realization.
A Successful Candidate Will Have:
- Deep understanding of institutional investment management and front\-office investment workflows, including research, portfolio management, and trading.
- Strong stakeholder management, facilitation, and executive communication skills.
- Experience leading AI, data, digital, product, or technology\-enabled change in investment management or capital markets.
- Credibility with investment professionals and senior investment leaders.
- Ability to translate investment workflow needs into AI priorities, roadmaps and adoption plans.
- Sound judgement balancing innovation, investment value, model and data risk, governance, feasibility, and adoption.
- Experience applying product\-oriented ways of working, including lifecycle management, prioritization, delivery alignment, adoption, and value measurement.
Key Role Characteristics
- Investment\-first AI leader : Starts with investment outcomes and workflows, not generic AI experimentation.
- Credible business owner : Can engage CIOs, asset class leaders, PMs, analysts, traders.
- Strategic translator : Connects investment workflow needs to AI\-enabled capabilities, data/technology delivery, and scalable adoption.
- Portfolio prioritizer : Frames use case trade\-offs based on investment value, risk, feasibility, reuse, and adoption readiness.
- Capability mobilizer : Mobilizes the expertise, resources, and sponsorship required to deliver the Investments AI roadmap and achieve business outcomes.
- Value and risk steward : Measures impact on investment workflows and escalates risks, policy questions, and executive decisions.
Salary Range:
$170,000 \- $267,500 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 $170K-$267K 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. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $170K to $267K.
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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