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About This Role
About the Role
Thomson Reuters is enhancing its Cyber Defense capability in response to an AI\-driven threat landscape that has fundamentally changed the speed, scale, and nature of cyberattacks.
The Distinguished Engineer, AI Threat Defense is an individual contributor and senior technical authority embedded within the existing 60\+ person Cyber Defense organization. Reporting to the VP of Cyber Defense, this individual will strengthen AI\-specific threat defense strategy and build AI\-augmented detection and response capability into the existing Security Operations Center (SOC) and Cyber Incident Response Team (CIRT).
This role has no direct reports or management authority . Instead, the individual will drive impact through technical credibility, architecture, hands\-on engineering, influence, and mentorship across SOC Operations, CIRT, Threat Detection Engineering, Vulnerability Management, Attack Surface Reduction, and Cyber Threat Management.
The role also owns two areas where the current organization lacks a dedicated deep technical owner: the technical strategy against AI\-specific attack vectors and the hands\-on architecture and delivery of AI\-augmented detection and response within the existing SOC and CIRT.
What You’ll Do
AI Threat Defense Strategy
- Own Thomson Reuters' monitoring and response strategy against AI\-specific attack vectors including prompt injection, Model Context Protocol (MCP) exploitation, agentic system compromise, deepfake\-enabled social engineering, and AI\-assisted reconnaissance .
- Partner with Security Engineering and Architecture to define standards for securing AI infrastructure and ensuring it can be effectively monitored and defended.
- Track emerging offensive AI capabilities, adversary tooling, published research, and threat\-actor adoption of AI so Cyber Defense capabilities evolve ahead of emerging threats.
Protect Thomson Reuters AI Applications and Systems
Drive monitoring and defenses for AI\-specific threat classes including:
- Direct and indirect prompt injection
- Jailbreaks and guardrail bypass
- Sensitive\-information and system\-prompt disclosure
- Unsafe model output and downstream execution
- Excessive agency and MCP/tool abuse
- Model and data poisoning
- AI supply\-chain compromise
- Model denial\-of\-service / denial\-of\-wallet attacks
- Model, prompt, and intellectual\-property theft
Own technical monitoring and response for Thomson Reuters AI\-enabled applications, features, and agentic services while partnering with Product Engineering, Security Engineering \& Architecture, and AppSec to embed security directly into the software development lifecycle.
Build AI\-Augmented Detection and Response
- Build AI\-augmented detection content and analytics for AI\-specific attack patterns.
- Integrate AI threat detection into the existing SIEM, detection engineering, and SOC monitoring stack .
- Build AI\-assisted playbooks and runbooks and integrate them into existing CIRT and SOAR workflows .
- Serve as the principal technical architect for incorporating AI capabilities into the existing 24/7 SOC and CIRT rather than creating a separate security function.
- Design automated workflows for AI\-related detection, triage, investigation, and response.
- Increase analyst productivity while maintaining human\-in\-the\-loop controls, audit trails, and rollback capability.
- Drive adoption across SOC Operations, CIRT, Threat Detection Engineering, Vulnerability Management, and Attack Surface Reduction.
Technical Leadership \& Influence
- Act as the recognized technical authority for AI threat defense across the Cyber Defense organization.
- Provide hands\-on mentorship and technical uplift to SOC, CIRT, engineering, and security professionals without formal management responsibility.
- Represent Cyber Defense externally with industry groups, customers, and regulators.
- Influence engineers and analysts through technical reviews, direct collaboration, architecture guidance, and knowledge sharing.
About You
You are a deeply technical cybersecurity leader who can operate at a Distinguished Engineer level without relying on formal organizational authority.
You combine strong hands\-on Cyber Defense expertise with practical knowledge of AI systems, AI\-specific attack vectors, and production security architecture.
You are comfortable working across mature enterprise security organizations and can influence senior executives, engineers, architects, SOC teams, incident responders, and other technical stakeholders.
You are equally comfortable developing strategy, architecting systems, building technical capabilities, mentoring engineers, and representing the organization externally.
Required Skills / Qualifications
- 12\+ years of progressive cybersecurity engineering experience , including experience operating at Principal, Staff, or Distinguished Engineer level within a large, complex enterprise.
- Deep hands\-on knowledge of AI\-specific attack vectors and defensive architectures .
- Production experience governing AI systems, agentic infrastructure, MCP or equivalent integration patterns .
- Proven experience architecting or delivering AI\-assisted detection and response within a mature 24/7 SOC and/or CIRT.
- Experience enhancing an established security organization rather than simply building a new function from scratch.
- Demonstrated ability to drive adoption of detection capabilities and security controls across:
+ SOC Operations
+ CIRT
+ Threat Detection Engineering
+ Vulnerability Management
+ Attack Surface Reduction
- Ability to influence technical teams through credibility rather than direct reporting authority.
- Exceptional communication skills, including the ability to explain AI\-related security risk to senior executives and technical audiences.
- Experience within a regulated, multi\-segment enterprise requiring coordination across legal, compliance, procurement, and governance organizations.
Preferred Qualifications
- Experience in financial services, legal technology, or professional information services .
- Experience embedding major technical capabilities into an existing mature security operations organization.
- Familiarity with frontier AI vulnerability research programs such as Anthropic Project Glasswing / Claude Code Security or comparable agentic vulnerability\-discovery initiatives.
- Hands\-on experience building or operating agentic AI systems using multiple LLMs , including open\-weight and hosted/frontier models.
- Experience integrating AI capabilities with SAST/AppSec tooling and vulnerability\-management workflows .
- Working knowledge of safely operating open\-weight or less\-restricted AI models for authorized internal security research.
- Published research, conference speaking, or active participation within AI security research communities.
- Relevant certifications such as CISSP, CISM , advanced AI/ML security, cloud security (AWS/Azure/GCP), or detection\-engineering credentials.
\#LI\-TH1
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.
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: $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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($311K) sits 45% above the category median. Disclosed range: $198K to $424K.
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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