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About This Role
Who we are looking for
State Street's Cyber Data \& Analytics (CyberDNA) team is seeking a Senior AI Security Automation Engineer to help shape the next generation of cybersecurity data, analytics, and AI\-powered platforms. Partnering closely with Global Cyber Security teams, Infrastructure Teams, and Enterprise Continuity Services, this team develops advanced data platforms, intelligent automation solutions, and engineering capabilities that enable cybersecurity teams to make faster, AI\-driven decisions and strengthen the firm's ability to detect, prevent, and respond to evolving cyber threats.
Why this role is important to us
Through innovation in AI, analytics, and automation, CyberDNA team plays a critical role in protecting State Street, its clients, and its partners from increasingly sophisticated global threat actors. This role sits at the intersection of Artificial Intelligence, Cybersecurity, Data Engineering, and Full\-Stack Software Development, with a primary focus on enabling secure, scalable, and governed AI adoption across the enterprise for supporting Cyber Security functions. The successful candidate will be part of a global AI security automation engineering team focused on delivering next\-generation AI platforms, autonomous agents, security automation solutions, and data\-driven applications that strengthen cyber resilience, accelerate innovation, and transform cybersecurity operations through intelligent automation.
What you will be responsible for
As Senior AI Security Automation Engineer, you will
- Drive the architecture and hands\-on delivery of scalable, reliable agentic AI platforms for security workflows
- Design and build production\-grade AI systems including agents, skills, memory patterns, guardrails, and tool\-use orchestration
- Architect retrieval and context\-engineering approaches including embeddings, semantic search, grounding, summarization, and prompt/version management
- Engineer cloud\-native AI services in AWS, Azure and GCP using containers and serverless patterns, event\-driven messaging, and distributed data stores
- Optimize platform performance across latency, throughput, scalability, caching, context efficiency, and cost controls
- Build well\-governed APIs and integrations that connect AI capabilities to security platforms, tools, and business processes
- Establish evaluation, research, regression testing, and observability frameworks to continuously improve quality and agent behavior
- Define engineering standards for reliability, security, and safe AI operation across the platform lifecycle
- Mentor junior engineers and influence engineering direction through code reviews, architecture forums, and cross\-team technical leadership
- Leverages enterprise\-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise\-authorized AI\-assisted development and automation capabilities, to improve the value realized by automation.
What we value
These skills will help you succeed in this role
- Demonstrated experience architecting, developing, and deploying production\-grade Generative AI and Large Language Model (LLM) based solutions, including agentic workflows, intelligent agents, and enterprise tool integration frameworks.
- Strong software engineering fundamentals with expertise in designing and delivering cloud\-native applications and services leveraging containers, serverless architectures, and modern public cloud platforms.
- Proven experience building highly scalable distributed systems utilizing asynchronous processing, event\-driven architectures, durable messaging, and high\-performance data access patterns.
- Hands\-on expertise developing Retrieval\-Augmented Generation (RAG) solutions, including embeddings, semantic search, knowledge grounding, context engineering, prompt optimization, and prompt lifecycle management.
- Experience implementing AI evaluation, testing, monitoring, and observability frameworks to measure model quality, reliability, performance, and safe operation in production environments.
- Strong API design and integration experience, including the development of secure, reusable, and scalable platform services that enable enterprise\-wide adoption of AI capabilities.
- Demonstrated technical leadership skills with a track record of mentoring engineers, driving architectural decisions, influencing technology strategy, and collaborating effectively with cross\-functional stakeholders.
- Hands\-on experience utilizing enterprise\-approved AI\-assisted software development tools to accelerate application delivery, improve code quality, streamline testing, and enhance documentation, while ensuring outputs are validated through secure coding practices, peer review, and automated testing.
- Strong understanding of responsible AI principles, including data privacy, security, governance, resiliency, and risk management, with the ability to guide teams in the safe and effective use of AI technologies.
- Deep understanding of cybersecurity data sources, including endpoint, network, application, cloud, and system telemetry, and their application in security monitoring, analytics, and automation.
- Experience working with Security Information and Event Management (SIEM) platforms and cybersecurity analytics solutions to ingest, correlate, retrieve, and analyze large\-scale security data.
- Strong understanding of cybersecurity operations, including threat detection, incident response, threat hunting, security analytics, and security automation.
- Proven ability to lead and influence geographically distributed teams through virtual collaboration, fostering strong partnerships, driving technical outcomes, and building effective relationships across engineering, security, and business organizations.
- Deep understanding of CI/CD tools (Jenkins, Harness, Spinnaker, Argo CD, etc.) and methodology, production experience in designing and implementing CI/CD pipelines with a focus on helping teams release frequently to production while maintaining deployment reliability
- Strong software development and automation skills with demonstrated experience in Python, JavaScript/TypeScript, Rust, Go (Golang), Bash, and PowerShell.
- Proven ability to lead complex technical initiatives while remaining deeply hands\-on.
- Experience developing cybersecurity, analytics, or operational intelligence solutions.
- Proven ability to lead complex technical initiatives while remaining deeply hands\-on.
- LangGraph, Semantic Kernel, CrewAI, AutoGen, LangChain, or similar frameworks.
- Databricks, Snowflake, Spark, Kafka, Delta Lake, Iceberg, and Airflow.
- Experience with vector databases, semantic search, and enterprise RAG platforms.
- Experience implementing MLOps, LLMOps, AI observability, and evaluation frameworks.
- Knowledge of Responsible AI, data governance, and model risk management.
Education \& Preferred Qualifications
- Master’s or bachelor’s degree in computer science, Software Engineering, Artificial Intelligence, Data Science, Cybersecurity, or a related discipline.
- 7\+ years of professional software engineering experience.
- 3\+ years of experience developing AI, Machine Learning, or Generative AI solutions.
- Demonstrated success designing and deploying enterprise\-scale applications and platforms.
- Experience developing cybersecurity, analytics, or operational intelligence solutions.
Work Requirement
- Work shift is 8 AM – 5 PM local time with occasional Level 2\-3 escalation resolution to support operational incidents
- This hybrid role includes an in\-office presence requirement of 2–4 days per week, consistent with the organization's hybrid work policy.
Salary Range:
$120,000 \- $202,500 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 $120K-$202K 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($161K) sits 26% below the category median. Disclosed range: $120K to $202K.
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
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