Interested in this AI/ML Engineer role at State Street?
Apply Now →Skills & Technologies
About This Role
About the RoleWe are seeking an experienced AI Systems Design Architect (VP) to lead enterprise AI system architecture strategy across the full Generative AI platform stack. This role requires deep expertise in Generative AI, enterprise architecture, platform engineering, distributed systems, and scalable cloud architectures on AWS, Azure, or Google Cloud Platform (GCP).
The ideal candidate will define architecture direction, technology standards, and cloud design principles for AI platforms and shared services across the organization. This role will shape modernization priorities, represent Architecture in senior leadership and governance forums, and ensure AI systems are designed for security, resiliency, compliance, interoperability, and long\-term scalability.
Key Responsibilities
- Own and drive enterprise\-wide AI system architecture strategy across the full GenAI platform stack, including shared services, orchestration layers, model integration, APIs, data flows, and downstream enterprise systems.
- Define and govern reference architectures, design standards, architecture principles, integration patterns, and cloud design approaches for AI platforms and AI\-enabled applications.
- Provide strategic technical leadership on scalable, secure, resilient, and compliant AI system design across the organization.
- Lead architecture decisions for cloud and hybrid AI environments on AWS, Azure, or Google Cloud, ensuring interoperability, platform reuse, cost efficiency, and operational excellence.
- Establish reusable enterprise patterns for RAG, model serving, prompt orchestration, agentic AI workflows, AI gateways, and service integration.
- Chair or lead architecture governance forums, reviewing major solution designs, resolving technical trade\-offs, and ensuring alignment to enterprise standards.
- Partner closely with business leaders, product teams, engineering, cloud, security, risk, data, and operations teams to align architecture strategy with business priorities.
- Ensure AI solutions comply with Responsible AI, governance, data controls, model risk, testing standards, security requirements, and regulatory expectations.
- Drive architecture assessments, technical due diligence, platform modernization, proof\-of\-concept direction, and production onboarding standards for AI systems.
- Guide enterprise decisions on platform capabilities, shared services, build\-vs\-buy evaluations, and modernization opportunities.
- Influence and mentor architects and engineering leaders to improve design quality, architectural consistency, and adoption of enterprise AI standards.
- Represent the Architecture function in senior governance and leadership forums.
Required Qualifications
- Bachelor’s degree in Computer Science, Computer Information Systems, Engineering, Mathematics, or a related discipline; Master’s degree preferred.
- 12–17 years of experience in solution architecture, enterprise application architecture, platform architecture, or AI/ML architecture, including leadership of large\-scale enterprise initiatives.
- Deep hands\-on and architectural experience with Generative AI, LLMs, ML systems, RAG, vector databases, prompt design/orchestration, and AI application integration.
- Proven expertise in cloud architecture and enterprise solution design on AWS, Azure, or Google Cloud Platform (GCP).
- Strong understanding of cloud\-native architecture including AI/ML services, networking, storage, security, IAM, resilience, and performance optimization.
- Proven experience designing enterprise\-scale, secure, resilient, and high\-performing architectures for AI and data\-driven applications.
- Expertise in API architecture, microservices, distributed systems, event\-driven patterns, enterprise integration, and platform design.
- Strong understanding of AI governance, Responsible AI, model risk, compliance, testing, production controls, and control frameworks.
- Strong understanding of DevSecOps, CI/CD, observability, monitoring, reliability engineering, and platform engineering concepts.
- Demonstrated ability to lead architecture discussions with senior executives and communicate effectively with both technical and non\-technical stakeholders.
- Strong leadership, stakeholder management, problem\-solving, and cross\-functional collaboration skills.
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.
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 $120K-$217K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($168K) sits 23% below the category median. Disclosed range: $120K to $217K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
State Street AI Hiring
State Street has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Quincy, MA, US, Boston, MA, US, Burlington, MA, US. Compensation range: $118K - $217K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.