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Summary of the Role
This leader turns our marketing AI strategy into working capability. The strategy and priorities are set; this role makes them real in production, at the quality bar a company serving legal, tax, and risk professionals demands.
You own AI skills development: the design, build, versioning, and governance of the library of AI skills and agents that power marketing workflows end to end. You lead a team of AI engineers and are a hands\-on builder yourself, not solely a manager. Delivering the strategy also depends on Teams you do not own, including engineering and data science functions; you will partner with and coordinate those Teams as dependencies, setting clear interfaces and holding delivery on track across them.
Success is not a longer list of tools or adoption dashboards. It is complete marketer workflows that work, from brief through content through channel, a foundation that is compliant and portable, and clear ownership of quality so AI improves outcomes without introducing regressions. Our internal AI credibility is a direct extension of our external brand, and this role protects it.
About the Role
- Own AI skills development: architect, build, version, and maintain the skills and agents that power marketing workflows, and set the standards others build to.
- Lead and grow a team of AI engineers, and stay hands\-on enough to build alongside them and set the technical bar.
- Convert the prioritized AI use\-case portfolio into shipped, measurable capability against committed timelines and quality standards.
- Manage cross\-functional dependencies: partner with and coordinate the engineering and data science Teams the strategy relies on, defining clear interfaces, sequencing, and accountability so delivery does not stall between teams.
- Deliver end\-to\-end workflows rather than fragments, enabling whole tasks from brief to finished, channel\-ready output.
- Build the compliance foundation first: legal, privacy, accessibility, and content\-integrity checks as reusable building blocks that everything else sits on top of.
- Establish engineering governance: a single accountable owner per skill, versioning and review discipline, monitoring for quality regressions, cost control, and guardrails for higher\-risk autonomous use cases.
- Engineer for portability and avoid platform lock\-in, so investment is not stranded by future platform decisions.
- Instrument outcomes that matter, including pipeline contribution, hours redeployed, time\-to\-insight, adoption and quality, and raise the standard for accountability and workflow redesign, not just speed.
- Partner with enterprise engineering, IT, and commercial and revenue operations so marketing AI integrates cleanly with the wider technology and commercial architecture.
About you
Required Qualifications
- Hands\-on experience architecting and shipping production AI applications: agentic workflows, skill and context engineering, orchestration, retrieval, and tool and API integration. You have built these systems, not just overseen them.
- A strong technical foundation in data engineering, analytics engineering, business intelligence, or software engineering: data pipelines, modern data platforms and warehouses, and the discipline to build for scale, quality, and governance. Deep data engineering experience counts fully here.
- A track record of building a capability or platform from scratch and scaling it to real, sustained usage.
- Experience leading engineers as a hands\-on builder\-leader, setting technical direction and raising the bar of those around you.
- Proven ability to deliver through influence and partnership across Teams you do not own, managing dependencies and holding cross\-functional delivery on track.
- Sound judgment on quality, risk, and governance in production AI, and the instinct to say no to work that will not clear a value or quality bar.
- Bachelor's degree or equivalent practical experience. We care more about what you have built than where you studied.
- Experience in marketing, growth, or commercial and Go\-to\-market contexts, or building tooling adopted by those functions.
Preferred Qualifications
- Familiarity with common marketing, data, and commercial technology stacks, including marketing automation, CRM, web and content platforms, and BI and data\-transformation tooling.
- Data science experience such as propensity, attribution, or segmentation modeling.
- Experience in a regulated or high\-accuracy industry, such as legal, tax, financial services, or healthcare, where compliance and correctness are non\-negotiable.
- A record of shipping AI or automation capability that a business function adopted at scale.
\#LI\-TV1
What’s in it For You?
- 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;For any eligible US locations, unless otherwise noted, the base compensation range for this role is $157,500 USD \- $292,500 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;
This job posting will close 10/12/2026\.
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.
We also make reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law. More information on requesting an accommodation here.
Learn more on how to protect yourself from fraudulent job postings here.
More information about Thomson Reuters can be found on thomsonreuters.com
Salary Context
This $157K-$292K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($225K) sits 5% above the category median. Disclosed range: $157K to $292K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Thomson Reuters AI Hiring
Thomson Reuters has 8 open AI roles right now. They're hiring across Research Engineer, AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span New York, NY, US, Eagan, MN, US, McLean, VA, US. Compensation range: $204K - $424K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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