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About This Role
About the Role
Retrieval is the ceiling on everything above it. An agent working a legal, tax, or regulatory question is only as good as the evidence handed to it, whether it can find the controlling authority in a corpus of millions of documents, weigh sources that conflict, and be honest about what it doesn't have. Every higher\-order capability we ship depends on retrieval being trustworthy first.
This role, in TR Labs, owns the engineering behind that layer: next\-generation search and retrieval serving both traditional search experiences and agentic AI workflows over large collections of legal, tax, and regulatory content. Multiple product teams depend on what it delivers.
What makes this research engineering rather than software engineering is that the answer isn't known when you start. Whether a different ranking model, a hybrid retrieval strategy, a new chunking scheme, or an agentic retrieval loop actually makes results better is an empirical question — and an easy one to get wrong, because a metric moving is not the same as retrieval improving. You form the hypothesis, isolate the variable, read the numbers honestly, and kill the idea when the data says to. Then you do the part many researchers don't: make the winning version production\-grade, ship it, and keep it healthy.
You will work shoulder\-to\-shoulder with applied scientists, building on their models and research directions and feeding production evidence back into the science. As a Lead you own end\-to\-end delivery, you deliver through the people around you, and you are the person we rely on to know the details, look around corners, and tell us early when something is going sideways.
About You
You are unusually rigorous with evidence. You reach for a baseline, an ablation, and a control before you trust a result, and you have the taste to know which experiments are worth running and which are not.
You have launched search systems, not just built them, but operated them, scaled them, debugged them at 2am, and measured whether they actually made retrieval better.
You build with AI tooling rather than around it, and you bring the same skepticism to what a coding agent hands you as to what an experiment tells you.
You don't wait to be handed a problem. Given a messy project, you can work out what the most impactful next thing to do is and go do it.
You can explain your work to engineers, scientists, and product stakeholders alike: defend a design choice, and update on evidence when someone shows you a better one.
What You'll Do
- Own end\-to\-end delivery of significant search and retrieval projects, accountable for the outcome, the quality and timeline, and the system once it is live
- Act as technical lead for a squad of 3–5 engineers: set direction, break down the work, review designs and code, and unblock the team
- Partner closely with applied scientists, build on their models, ranking approaches, and research directions, and feed production evidence back into the science
- Run the exploration POC proof of value productionization loop, and decide what to try next, including what not to try
- Design and build retrieval architectures, ingestion and indexing pipelines, and ranking and re\-ranking systems on OpenSearch and Vespa
- Build the retrieval infrastructure that agentic AI workflows depend on, and the search agents themselves: tool\-facing retrieval APIs, agentic query planning and multi\-step retrieval, RAG pipelines, hybrid and semantic retrieval, and query understanding
- Build evaluation that actually discriminates — offline relevance harnesses, golden and labeled sets, online A/B tests, and end\-to\-end agent quality measurement designed to separate real improvement from a number that happened to move, and to keep discriminating as the models get stronger
- Diagnose retrieval and agent quality failures: why is this result wrong, which stage of the pipeline caused it, and what does that imply about the design
- Build and operate production APIs and backend services on AWS, with the performance, reliability, and cost characteristics that mission\-critical systems require
- Identify and communicate risk to timelines and architecture early and clearly, to peers and to senior stakeholders
- Influence architecture decisions beyond your own squad through design review, alignment with partner teams, and mentorship.
Minimum Qualifications
- Bachelor's or Master's in Computer Science, Engineering, or a related field
- \~7\+ years building production software, including search, retrieval, or ranking systems you shipped and then owned — launched, scaled, and maintained, not just prototyped
- Proven track record leading technical projects and delivering through other engineers, and influencing architecture decisions across teams
- Deep hands\-on production expertise in OpenSearch or Vespa (or comparable depth in Elasticsearch, Solr, or Lucene, with the ability to ramp on ours) rather than only consuming a vector database or a retrieval API
- Rigor with evidence: designing search experiments, relevance and ranking metrics, offline evaluation harnesses, online A/B measurement — and the discipline to know when a result is real
- Outstanding software engineering in Python, across the stack from ingestion pipelines to retrieval services to evaluation infrastructure
- AI\-native development: agentic coding tools are a routine part of how you build, and you have judgment about where they make you faster and where their output needs checking before it ships
- Designing, operating, and scaling production APIs and large\-scale distributed systems on AWS, including performance optimization at scale
- Information retrieval fundamentals: indexing and ingestion at large corpus scale, vector search, embeddings, semantic and hybrid retrieval, and RAG infrastructure built for production use
- Track record of collaborating with applied scientists or ML practitioners and productionizing their models and approaches
Preferred Qualifications
- Search relevance and ranking depth: query understanding, learning\-to\-rank, LLM\-based ranking or re\-ranking
- LLM\-as\-judge or model\-assisted relevance evaluation, and evaluating open\-ended or knowledge\-intensive LLM behavior
- Retrieval systems and search agents purpose\-built for agentic AI workflows
- Kafka, event\-driven architectures, and large\-scale data pipelines
- ML infrastructure, embedding pipelines, and vector databases
- Operating mission\-critical production systems with meaningful SLAs
- Experience with legal, regulatory, tax, scientific, or other text\-heavy domains
- Self\-service platform capabilities consumed by internal product teams
\#LI\-TH1
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.\&\#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 $158,000 USD \- $293,000 USD.\&\#xa;For any eligible US locations, unless otherwise noted, the base compensation range for this role is $137,100 USD \- $254,700 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 $137K-$293K range is above the median for Research Engineer roles in our dataset (median: $207K across 63 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 4,317 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Thomson Reuters, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $272,100 based on 227 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($215K) sits 21% below the category median. Disclosed range: $137K to $293K.
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 AI Engineering Manager ($244,000). 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
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
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).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
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.
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