Lead Research Engineer, Search & Retrieval

Thomson Reuters•Frisco, TX

9d•$137,100 - $293,000•Hybrid

About The Position

This role in TR Labs owns the engineering behind next-generation search and retrieval systems serving both traditional search experiences and agentic AI workflows over large collections of legal, tax, and regulatory content. Multiple product teams depend on the output of this role. The research engineering aspect involves empirical questions about the effectiveness of different ranking models, hybrid retrieval strategies, chunking schemes, or agentic retrieval loops. The role requires forming hypotheses, isolating variables, analyzing data, and then making the winning solutions production-grade and maintaining them. The Lead will work closely with applied scientists, building on their models and research, and feeding production evidence back into the science. This role owns end-to-end delivery, influences others, and is responsible for understanding details, anticipating issues, and identifying problems early.

Requirements

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

Nice To Haves

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

Responsibilities

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.

Benefits

Hybrid Work Model
Flexibility & Work-Life Balance (including work from anywhere for up to 8 weeks per year)
Career Development and Growth (Grow My Way programming)
Industry Competitive Benefits (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)
Globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more.
Social Impact (two paid volunteer days off annually, pro-bono consulting projects, and ESG initiatives)
401k plan with company match
Health, dental, vision, disability, and life insurance programs
Competitive vacation, sick and safe paid time off, paid holidays
Parental leave
Sabbatical leave
Flexible Spending and Health Savings Accounts
Fitness reimbursement
Access to Employee Assistance Program
Group Legal Identity Theft Protection
Access to 529 Plan
Commuter benefits
Adoption & Surrogacy Assistance
Tuition Reimbursement
Access to Employee Stock Purchase Plan