Interested in this AI/ML Engineer role at State Street?
Apply Now →Skills & Technologies
About This Role
AIDeveloper \- AVP
Who we are looking for
The AI Engineer will play a key role in advancing AI\-enabled capabilities across the Charles River Investment Management Solution and Alpha Platform. This individual will design, build, and scale production\-grade AI systems, including copilots, agentic workflows, and automation services embedded directly into investment management processes.
To be successful, the candidate must be a hands\-on engineer with strong experience in LLMs, AI orchestration, and distributed systems, capable of translating emerging AI patterns (e.g., RAG, agent frameworks, MCP integration) into secure, scalable enterprise solutions. This role requires close collaboration with product owners, architects, and domain teams to operationalize AI across client\-facing and internal workflows.
Why this role is important to us
- Enables Scalable AI Adoption: Builds reusable platforms and services that allow product teams to rapidly integrate AI without fragmentation
- Drives Business Impact: Embeds AI into front\-to\-back workflows, improving productivity, automation, and decision\-making for clients
- Bridges Innovation and Production: Converts emerging AI technologies into secure, governed, production\-ready capabilities
- Accelerates Time\-to\-Market: Enables faster delivery of high\-value AI use cases through standard architecture and tooling
What you will be responsible for
- Design, develop, and deploy AI\-powered services, including copilots, agent\-based workflows, and automation tools
- Build and integrate LLM\-based solutions using orchestration frameworks and tool\-calling patterns
- Implement RAG pipelines using enterprise data sources and vector databases
- Develop and integrate multi\-agent systems using MCP servers, APIs, and A2A based tooling
- Embed AI capabilities into core CRD and Alpha workflows across front, middle, and back\-office processes
- Build reusable AI enablement platforms, SDKs, and shared services for product teams
- Integrate AI services with cloud platformsand enterprise systems
- Expose AI capabilities through API Management layers and event\-drivenarchitectures
- Ensure performance, scalability, and reliability of AI systems in production environments
- Implement monitoring, evaluation, and observability for AI models and pipelines
- Apply responsible AI practices (security, explainability, compliance, governance)
- Collaborate with product, architecture, data science, and UX teams to deliver end\-to\-end solutions
- Participate in agile development processes including sprint planning, code reviews, and retrospectives
What we value
- Strong expertise in LLMs, prompt engineering, and AI system design
- Experience building AI copilots, conversational interfaces, or agent\-based systems
- Hands\-on experience with RAGarchitectures, embeddings, and vector databases
- Familiarity with MCP frameworks, tool\-calling patterns, and agent orchestration
- Experience with cloud\-native AI services (Azure preferred) and distributed architectures
- Proficiency in Python and at least one additional language (Java, C\#, or TypeScript)
- Experience with API\-drivenarchitectures, microservices, and event streaming (Kafka, Event Hub)
- Knowledge of distributed caching (Redis, Hazelcast) and performance optimization patterns
- Strong software engineering fundamentals: testing, CI/CD, code quality, and design patterns
- Familiarity with AI governance, model risk management, and security best practices
- Ability to work across strategic and hands\-on engineering tasks
- Strong collaboration and communication skills in cross\-functional environments
Education \& Preferred Qualifications
- B.S. or M.S. in Computer Science, Engineering, Mathematics, or related field
- 2–5\+ years of software engineering experience, with a focus on building scalable systems
- Experience delivering AI/ML\-powered applications into production
- Strong understanding of modern AI architectures (LLMs, RAG, APIs, orchestration layers)
- Experience working with cloud platforms (Azure, AWS, or GCP)
- Preferred:
- Experience in investment management, trading systems, or financial data platforms
- Experience with AI agent frameworks, MCP servers, or advanced orchestration patterns
- Experience working within Agile/Scrum teams following modern SDLC practices, including secure coding standards, CI/CD
About CRD
Charles River Development (CRD) is the FinTech division of State Street. Together with State Street’s middle\- and back\-office services, Charles River’s cloud\-based front\-office technology forms the foundation of the State Street Alpha® Platform \- the first front\-to\-back solution in the industry.
Our vision is to be the world’s leading investment platform, driving innovation, resiliency, and growth for investment firms globally. CRD Engineering is transforming the platform into a modern, cloud\-native ecosystem leveraging Microsoft Azure, Kafka, Snowflake, and advanced AI technologies.
As part of this transformation, AI Enablement is a strategic priority \- bringing together data, engineering, and intelligent automation to redefine investment workflows and deliver measurable client value.
About State Street
What we do. State Street is one of the largest custodian banks, asset managers and asset intelligence companies in the world. From technology to product innovation we’re making our mark on the financial services industry. For more than two centuries, we’ve been helping our clients safeguard and steward the investments of millions of people. We provide investment servicing, data \& analytics, investment research \& trading and investment management to institutional clients.
Work, Live and Grow. We make all efforts to create a great work environment. Our benefits packages are competitive and comprehensive. Details vary in locations, but you may expect generous medical care, insurance and savings plans among other perks. You’ll have access to flexible Work Program to help you match your needs. And our wealth of development programs and educational support will help you reach your full potential.
Inclusion, Diversity and Social Responsibility. We truly believe our employees’ diverse backgrounds, experiences and perspective are a powerful contributor to creating an inclusive environment where everyone can thrive and reach their maximum potential while adding value to both our organization and our clients. We warmly welcome the candidates of diverse origin, background, ability, age, sexual orientation, gender identity and personality. Another fundamental value at State Street is active engagement with our communities around the world, both as a partner and a leader. You will have tools to help balance your professional and personal life, paid volunteer days, matching gift program and access to employee networks that help you stay connected to what matters to you.
State Street is an equal opportunity and affirmative action employer.
Discover more at StateStreet.com/careers
Salary Range:
$90,000 \- $157,500 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.
Discover more information on jobs at StateStreet.com/careers
Read our CEO Statement
Job Application Disclosure:
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 $90K-$157K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($123K) sits 33% below the category median. Disclosed range: $90K to $157K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
State Street AI Hiring
State Street has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Quincy, MA, US, Burlington, MA, US, Boston, MA, US. Compensation range: $91K - $217K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.