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Quality Assurance Engineer – AI Testing, Charles River Development/State Street Alpha
Why this role is important to us
The team you will be joining is responsible for advancing quality engineering for AI-enabled capabilities within the CRIMS / State Street Alpha product suite. As a Quality Assurance Engineer focused on AI testing, you will work within a scrum team to design and implement scalable test frameworks, validate AI-driven product behavior, and strengthen the team’s ability to test intelligent features with speed, rigor, and confidence. This role is ideal for a mid-level QA professional who combines strong software testing fundamentals with hands-on experience applying AI in the test process and testing AI functionality embedded in enterprise products.
Role Description
- Partner closely with product managers, software engineers, architects, and scrum team members to ensure AI-enabled capabilities are testable, reliable, and aligned to business and client needs.
- Design and build reusable QA frameworks and test approaches tailored to validating AI-driven functionality, including model-integrated workflows, intelligent recommendations, generated outputs, and workflow automation.
- Define test strategies that address both traditional application quality and AI-specific quality dimensions such as consistency, accuracy, robustness, explainability, and edge-case handling.
- Contribute to sprint planning, story refinement, and acceptance criteria development to ensure quality considerations are embedded early in the software development lifecycle.
- Help evolve the team’s quality engineering practices by introducing practical uses of AI to improve test design, coverage analysis, defect detection, and test execution efficiency.
Responsibilities
- Develop and maintain automated and manual test frameworks that support validation of AI-enabled features across the CRIMS / Alpha platform.
- Create test plans, test cases, and test data strategies for functional, integration, regression, and non-functional testing of AI-driven capabilities.
- Validate product behavior for AI use cases, including quality of outputs, workflow integration, response accuracy, exception handling, and user experience impacts.
- Design and execute tests for AI guardrails, probabilistic outputs, and response boundaries to ensure generated results are accurate, safe, consistent, and aligned with intended business outcomes.
- Help define approaches for monitoring AI behavior in production, including drift detection, output quality trends, and feedback loops to confirm AI-enabled features continue learning and performing as expected over time.
- Identify, document, and drive resolution of defects, with clear analysis of reproducibility, business impact, and root cause patterns.
- Work with engineering teams to integrate automated testing into CI/CD pipelines and improve test coverage across the scrum team’s delivery scope.
- Apply AI tools and techniques within the test lifecycle to improve productivity, accelerate coverage creation, and enhance defect discovery.
- Collaborate with developers and product partners to define measurable acceptance criteria and quality gates for AI-related user stories and features.
- Support risk-based testing and help prioritize test efforts across new development, regression areas, and high-impact client workflows.
- Contribute to continuous improvement of QA standards, test assets, documentation, and team-level best practices for testing AI-enabled products.
- Communicate testing progress, risks, and quality insights effectively within the scrum team and to broader stakeholders as needed.
Skills Required
- 0-2 years of professional software testing or quality assurance experience, preferably within agile or scrum-based product development teams.
- Proven experience implementing AI capabilities within the software testing process, such as intelligent test generation, test optimization, defect analysis, or productivity acceleration.
- Critical to the role is hands-on experience testing AI functionality embedded within a software product, including validation of generated outputs, model-driven behavior, workflow integration, and exception scenarios.
- Understanding of AI-specific testing concepts beyond deterministic validation, including guardrail testing, probabilistic output assessment, accuracy evaluation, and monitoring for production drift or model behavior changes over time.
- Strong understanding of QA methodologies, test strategy development, defect lifecycle management, and test automation best practices.
- Experience building or extending test frameworks to support scalable, maintainable, and reusable testing practices.
- Experience with API testing, integration testing, regression testing, and end-to-end validation in complex enterprise applications.
- Working knowledge of test automation tools, scripting, and CI/CD integration.
- Strong analytical, problem-solving, and communication skills, with the ability to work effectively across cross-functional teams.
- Ability to translate ambiguous product behavior into clear test scenarios, acceptance criteria, and risk-based validation approaches.
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
- Financial services, asset management, trading, investment management, or fintech experience is a plus.
Additional Information
Charles River Development, a State Street company, partners with clients to deliver enterprise investment management technology solutions. Together with State Street Alpha, CRD helps power front-to-back investment workflows through innovation, scale, and deep financial domain expertise. In this role, you will help ensure the quality and reliability of next-generation AI capabilities that support our clients’ evolving needs.
Salary Range:
$80,000 - $140,000 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 $80K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($110K) sits 48% below the category median. Disclosed range: $80K to $140K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
State Street AI Hiring
State Street has 18 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Cracow, Małopolskie, Poland, Quincy, MA, US, Boston, MA, US. Compensation range: $118K - $217K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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