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About This Role
About the Role
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Thomson Reuters is investing in AI as a core capability across Legal, Tax, Risk, News, and Corporates. We are seeking a senior Technology Leader to define and drive the technical direction for how enterprise AI systems—including multi‑agent architectures, fine‑tuned models, and retrieval‑augmented solutions—are designed, governed, and scaled across the platform organization, while ensuring product engineering teams can safely and efficiently consume these capabilities.
This role leads a global AI Platform Engineering organization responsible for delivering AI Engineering Services. Positioned within Platform Engineering, it operates in close partnership with AI Labs, Product Engineering, Information Security, Legal, and Procurement to establish production\-grade AI platforms and standards.
You will serve as the technical authority across three integrated domains: Enterprise AI Engineering Services, AI‑Native Developer Enablement, and AI Platform Integration—ensuring cohesive, secure, and scalable adoption of AI capabilities across Thomson Reuters. This includes leading a diverse global team of 35\+ engineers and driving engineering rigor, platform standardization, and responsible AI practices.
As Vice President, AI Platform Engineering , you will report to the Head of Platform Engineering and be a key member of a high\-performing organization at the center of Thomson Reuters’ technology and AI strategy. You will own:
AI\-Native Engineering and Developer Enablement
You will define what AI\-native development means at Thomson Reuters: the agreed coding\-agent stack, the prompt and evaluation standards, the CI/CD integrations, and the guardrails that make agent\-assisted development safe inside the enterprise. You will govern the AI developer tooling estate (coding agents, prototyping tools, evaluation and observability platforms) and set patterns for MCP servers and developer\-facing agents.
AI Platform and Tooling Integration
You will own the technical direction for the AI platform capabilities engineering teams across TR consume: model serving, evaluation infrastructure, RAG and retrieval infrastructure, fine\-tuning workflows, prompt and observability tooling, and the integration of these capabilities with the existing platform estate (IDP, API management, observability, GitHub). You will partner with and align the Cloud Infrastructure agentic platform team to keep developer\-facing and ops\-facing AI strategies coherent.
Performance \& Scalability
You will own the quality bar for security, scalability, and reliability of AI Platform Systems. Define and enforce observability and performance standards for AI\-driven workloads operating at enterprise scale across millions of documents and interactions.
Collaboration \& Leadership
You will lead and grow teams of AI engineers building the services and platforms that translate AI capabilities into real\-world product applications. Foster a culture of experimentation, continuous improvement, and engineering excellence. Own hiring, performance, and mentorship across the organization.
Key responsibilities and impact
- Define, publish, and evolve TR’s AI‑native engineering standards, including reference implementations; support adoption through office hours and direct engagement with product engineering teams.
- Partner with InfoSec, Legal, Privacy, and the AI Council to ensure AI engineering practices are auditable, compliant, and production ready.
- Lead architecture reviews for AI systems entering the production estate, driving standards compliance and shaping the Platform Engineering portfolio ahead of demand.
- Lead technical evaluations and build‑vs‑buy decisions for AI infrastructure, model serving, evaluation platforms, and developer tooling.
- Represent Platform Engineering in M\&A technical due diligence, where AI, cloud, and modern engineering posture are in scope.
- Drive broader engineering productivity and developer experience initiatives where AI intersects with IDP, DORA, API‑first delivery, and Consumer Success engagements.
- Coach and mentor principal and staff engineers across the organisation, raising the bar for how TR designs, evaluates, and delivers production\-grade AI systems.
- Own the publication of TR’s AI\-native reference implementations and drive adoption across product engineering teams.
- Represent Thomson Reuters externally at AI engineering, platform, and architecture forums to both learn from and influence industry best practices.
Location
This hybrid role can be based in any of the following hub locations: Eagan, MN; Frisco, TX or New York, NY.
About You
=============
You are a strong fit for the VP, AI Platform Engineering role if you possess the following skills and experience:
- 15\+ years building and operating software at enterprise scale, with at least 5 years in AI/ML engineering with a focus on production deployment.
- Proven track record building and shipping multi\-agent AI systems at enterprise scale.
- Deep expertise in LLM fine\-tuning techniques (DPO, RAG, multi\-turn prompting).
- Experience designing and implementing agentic AI architectures: tool\-use, orchestration, reasoning, evaluation.
- Strong background in MLOps and ML data infrastructure (Spark, Kafka, Kubernetes, model serving, feature stores).
- Working fluency across the current AI\-developer\-tooling landscape: coding agents, MCP, evaluation frameworks, prompt and eval observability. You should be using this stuff yourself, not just reading about it.
- Experience with cloud AI services across AWS (Bedrock, SageMaker), Azure, GCP, or OCI.
- Proficiency in Python and modern ML frameworks (PyTorch, HuggingFace, LangChain, AutoGen, vLLM).
- Deep engineering credibility with hands\-on AI engineering experience. You carry the technical depth to lead architecture reviews, set the standard, and drive engineering quality across teams.
- Familiarity with engineering productivity frameworks (DORA, SPACE, DX) and the limits of each.
- Comfort operating across CTO and CIO governance, product engineering leaders, security, legal, and procurement. You know how to land AI strategy in a regulated, multi\-segment business.
- Strong written communication. You will be writing standards, not just slide decks.
- Demonstrated ability to lead research teams and translate academic work into production systems.
- Track record of organizational leadership and scaling cross\-functional teams.
Nice to have
- Graduate degree in Computer Science, AI, Data Science, or related field.
- Active academic researcher or open\-source contributor in agentic AI, LLM customisation, or related areas.
- Experience with conversational AI, document intelligence (LayoutLM\-style models), or domain\-specific dialogue systems.
- Prior experience in leading teams of engineers, AI researchers, and product development teams to translate AI capabilities into real\-world applications, owning large projects or workstreams.
- Experience in legal, tax, financial services, or other regulated information businesses.
- Experience defining and shipping data catalogues and workflows at scale (Port, Backstage, or comparable).
What success looks like in the first 12 months
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- TR has a published AI engineering standards document and reference architecture for multi\-agent systems, adopted by at least three product or platform teams.
- A defended set of evaluation, observability, and production\-readiness criteria exists for AI systems entering the TR production estate.
- The AI developer tooling estate is rationalised, governed, and procured under enterprise terms rather than team\-by\-team purchase.
- TR's AI\-native development standard is published, with measurable adoption across the top 12 master products.
- TR's AI\-native reference implementations are published and adopted across product engineering teams.
- Production quality of AI systems has improved, reflected in fewer architectural exceptions, stronger auditability, and faster time\-to\-approval for compliant systems.
\#LI\-PFF
What’s in it For You?
- Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2\-3 days a week in the office depending on the role) 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.
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.\&\#xa;\&\#xa;Eligible office location(s) for this role include one or more of the following: New York City, San Francisco, Los Angeles, and/or Irvine, CA; McLean, VA; Washington, DC. The base compensation range for the role in any of those locations is $228,000 USD \- $424,000 USD.\&\#xa;For any eligible US locations, unless otherwise noted, the base compensation range for this role is $198,200 USD \- $368,000 USD.\&\#xa;\&\#xa;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.\&\#xa;This role may also be eligible for an Annual Bonus based on a combination of enterprise and individual performance.\&\#xa;
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 HR.Leave\[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 $198K-$424K range is above the 75th percentile 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 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($311K) sits 42% above the category median. Disclosed range: $198K to $424K.
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.
Thomson Reuters AI Hiring
Thomson Reuters has 5 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Eagan, MN, US, New York, NY, US. Compensation range: $172K - $424K.
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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