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About This Role
Job Description:
Engineering Manager,
Are you passionate about transforming the future of legal research with AI? Join our Global Westlaw Engineering organization and help shape the next generation of intelligent legal research solutions.
The Global Westlaw Engineering organization builds and operates the technology that powers Westlaw, the world's leading legal research platform. Our teams develop innovative products and fiduciary-level, AI-based solutions that help legal professionals find trusted answers, uncover insights, and make confident decisions faster. Working across engineering, AI, data, platform, and product disciplines, we deliver secure, scalable, and intelligent experiences for customers around the globe.
As an Engineering Manager, you will build and lead a team of software engineers, including Senior and Staff levels, to deliver the full-stack products, AI-enabled features, and services behind Westlaw's next generation of experiences. Your team will build agentic legal research experiences and the retrieval services that ground them in trusted legal content.
You will stay close to the technology as a hands-on leader. You will help shape architecture, set engineering standards, and guide your team in building with agentic systems, retrieval-augmented generation (RAG), and large language models (LLMs). You will also champion AI-assisted development as a way of working, so your team can deliver intelligent, trustworthy, and high-performing solutions for legal professionals worldwide.
Key Responsibilities
Team Management
- Build and lead a team of software engineers, including Senior and Staff levels, owning hiring, onboarding, performance management, and professional development, with a focus on deep technical ownership.
- Develop engineers with strong software fundamentals and domain expertise in information retrieval and agentic system design.
- Foster a culture of empowered ownership in which engineers define problems, make sound technical decisions, and carry work through to reliable production operations.
- Shift team mindsets and build a culture of continuous learning, helping engineers adopt new tools, practices, and ways of working.
- Lead by example as a hands-on technical manager, writing and reviewing code, prototyping solutions, and resolving critical production issues.
- Translate complex technical concepts and trade-offs into clear terms for stakeholders.
Technical Leadership
- Oversee the development of scalable user interfaces, backend services, and RESTful APIs.
- Drive GenAI and large language model (LLM) integration initiatives.
- Shape architecture, engineering standards, coding practices, and code review processes.
- Champion a DevOps-first mindset in which engineers own build, deployment, monitoring, and reliability, supported by automation and CI/CD.
- Ensure delivery meets both functional and non-functional requirements, including performance, scalability, reliability, security, and maintainability.
- Embed quality and security into every stage of development, with rigorous testing, secure coding practices, and continuous evaluation of quality.
- Design and manage cloud-native solutions on AWS.
- Evaluate and integrate emerging tool protocols (such as MCP) and retrieval patterns, with a clear understanding of production readiness and operational implications.
AI-Enabled Engineering
- Model AI-assisted and agent-driven development as a team norm, and monitor and optimize token consumption to balance productivity, quality, and cost.
- Lead feature development using AI agents, setting clear practices for task specification, human review of agent-generated code, and accountability for what ships.
- Apply AI across the engineering lifecycle, including code generation, code review, test automation, and quality evaluation, with guardrails for security, data handling, and responsible use.
- Define how the team measures the impact of AI and agents on delivery speed, code quality, and defect rates.
Strategic Delivery
- Define and maintain secure engineering processes for the team that builds and delivers our flagship legal research products.
- Deliver solutions aligned with product roadmaps through incremental delivery, managing dependencies and tracking success metrics.
Continuous Learning
- Stay current with developments in AI, cloud computing, and software engineering, and bring them into the team's practices.
Required Qualifications
- 8+ years of hands-on software engineering experience across front-end and back-end, building scalable web applications, with 3+ years managing engineering teams.
- A track record of leading teams that ship and operate production-grade full-stack systems at scale, including secure coding practices and reliability, and of staying hands-on through architecture discussions, design decisions, code reviews, and prototyping.
- Hands-on experience building with LLMs in production or in serious prototypes, such as RAG, prompt and context design, evaluation, or agent workflows, and daily use of AI coding tools (e.g., GitHub Copilot, Cursor, Claude Code).
- Experience leading, or working hands-on in, a team that builds and uses AI agents, with strong practices for review, quality, and security.
- Clear understanding of RESTful API design principles, including: resource modeling and URI designcorrect use of HTTP methods and status codesidempotencypagination, filtering, and versioningauthentication and authorizationconsistent error handlingAPI documentation (e.g., OpenAPI/Swagger)
- Strong proficiency in Python and in designing and building RESTful APIs.
- Working proficiency in React or a similar modern UI framework (such as Angular or Vue), and experience with TypeScript and Node.js.
- Strong knowledge of AWS architecture and services, with experience in DevOps, CI/CD pipelines, and infrastructure as code.
- Success in hiring, coaching, mentoring, and developing engineers, including Senior and Staff levels.
- Experience partnering with Product, Design, and cross-functional leaders to set strategy and execute against it.
- Excellent communication skills, with the ability to set clear goals, give direct feedback, and align stakeholders.
- Comfort operating in a fast-moving, ambiguous environment, making data-driven decisions as priorities evolve.
- A strategic mindset, with the ability to ensure technical decisions align with business goals.
- A Bachelor's degree in computer science, engineering, or a related discipline, or equivalent practical experience.
Preferred Qualifications
- Experience shipping AI-driven or agentic systems in production, such as LLM orchestration, RAG and information retrieval, agent frameworks, and tool protocols (e.g., MCP), using LLM APIs (OpenAI, Anthropic) reliably at scale.
- Experience with agent evaluation, including testing, benchmarking, and monitoring agent behavior.
- Experience with Python frameworks such as Django, Flask, or FastAPI.
- Background managing mixed teams of front-end, back-end, and software/AI engineers.
- A track record of changing team mindsets and helping engineers adopt new tools and ways of working.
- Exposure to legal or other regulated professional domains.
AWS certifications, such as Solutions Architect or DevOps Engineer. *
#LI-SM2
This posting is for proactive recruitment purposes and may be used to fill current openings or future vacancies within our organization.
What’s in it For You?
- Hybrid Work Model: We’ve adopted a flexible hybrid working environment for our office-based roles while delivering a seamless experience that is digitally and physically connected.
- Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance.
- Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow’s challenges and deliver real-world solutions. Our Grow My Way programming and skills-first approach ensures you have the tools and knowledge to grow, lead, and thrive in an AI-enabled future.
- Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing.
- Culture: Globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more. We live by our values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together.
- Social Impact: Make an impact in your community with our Social Impact Institute. We offer employees two paid volunteer days off annually and opportunities to get involved with pro-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives.
- Making a Real-World Impact: We are one of the few companies globally that helps its customers pursue justice, truth, and transparency. Together, with the professionals and institutions we serve, we help uphold the rule of law, turn the wheels of commerce, catch bad actors, report the facts, and provide trusted, unbiased information to people all over the world.
Our use of AI within the recruitment process Thomson Reuters utilizes Artificial Intelligence (AI) to support parts of our global recruitment process. Unless you opt-out, our AI system will assess the information provided by you and compare it to the requirements listed for the role, and present the result to our recruitment personnel for further review. The AI system acts as a supporting tool, but there is always a human making the decision if you will be considered for the role.
In the United States, Thomson Reuters offers a comprehensive benefits package to our employees. Our benefit package includes market competitive health, dental, vision, disability, and life insurance programs, as well as a competitive 401k plan with company match. In addition, Thomson Reuters offers market leading work life benefits with competitive vacation, sick and safe paid time off, paid holidays (including two company mental health days off), parental leave, sabbatical leave. These benefits meet or exceeds the requirements of paid time off in accordance with any applicable state or municipal laws. Finally, Thomson Reuters offers the following additional benefits: optional hospital, accident and sickness insurance paid 100% by the employee; optional life and AD&D insurance paid 100% by the employee; Flexible Spending and Health Savings Accounts; fitness reimbursement; access to Employee Assistance Program; Group Legal Identity Theft Protection benefit paid 100% by employee; access to 529 Plan; commuter benefits; Adoption & Surrogacy Assistance; Tuition Reimbursement; and access to Employee Stock Purchase Plan. Thomson Reuters complies with local laws that require upfront disclosure of the expected pay range for a position. The base compensation range varies across locations.

For any eligible US locations, unless otherwise noted, the base compensation range for this role is $118,400 USD - $219,800 USD.
For Ontario, Canada, the base compensation range for this role is $140,600 CAD - $190,600 CAD.

Base pay is positioned within the range based on several factors including an individual’s knowledge, skills and experience with consideration given to internal equity. Base pay is one part of a comprehensive Total Reward program which also includes flexible and supportive benefits and other wellbeing programs.
This role may also be eligible for an Annual Bonus based on a combination of enterprise and individual performance.
 About Us
Thomson Reuters informs the way forward by bringing together the trusted content and technology that people and organizations need to make the right decisions. We serve professionals across legal, tax, accounting, compliance, government, and media. Our products combine highly specialized software and insights to empower professionals with the data, intelligence, and solutions needed to make informed decisions, and to help institutions in their pursuit of justice, truth, and transparency. Reuters, part of Thomson Reuters, is a world leading provider of trusted journalism and news.
We are powered by the talents of 26,000 employees across more than 70 countries, where everyone has a chance to contribute and grow professionally in flexible work environments. At a time when objectivity, accuracy, fairness, and transparency are under attack, we consider it our duty to pursue them. Sound exciting? Join us and help shape the industries that move society forward.
As a global business, we rely on the unique backgrounds, perspectives, and experiences of all employees to deliver on our business goals. To ensure we can do that, we seek talented, qualified employees in all our operations around the world regardless of race, color, sex/gender, including pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law. Thomson Reuters is proud to be an Equal Employment Opportunity Employer providing a drug-free workplace.
Thomson Reuters makes reasonable accommodations for applicants with disabilities, including veterans with disabilities, and for sincerely held religious beliefs in accordance with applicable law. If you reside in the United States and require an accommodation in the recruiting process, you may contact our Human Resources Department at [email protected] . Disability accommodations in the recruiting process may include things like a sign language interpreter, making interview rooms accessible, providing assistive technology, or other relevant accommodations. Please note this email is not intended for general recruitment questions and we will promptly respond to inquiries regarding accommodations. More information on requesting an accommodation here.
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More information about Thomson Reuters can be found on thomsonreuters.com
Salary Context
This $118K-$219K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Thomson Reuters, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($169K) sits 20% below the category median. Disclosed range: $118K to $219K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Thomson Reuters AI Hiring
Thomson Reuters has 37 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Software Engineer, Data Scientist. Positions span Eagan, MN, Eagan, MN, US, New York, NY. Compensation range: $137K - $474K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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