Lead Research Engineer, Search & Retrieval
Thomson Reuters · New York +1 · 1mo ago
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.

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.
For any eligible US locations, unless otherwise noted, the base compensation range for this role is $137,100 USD - $254,700 USD.

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.

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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.
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