Lead Research Engineer – AI Evaluation & Benchmarking

Thomson Reuters•Frisco, TX

6d•$127,400 - $272,600•Hybrid

About The Position

Thomson Reuters Labs is seeking a Lead Research Engineer to lead the design and delivery of production-ready software for AI model evaluation and benchmarking. The role builds scalable evaluation infrastructure that enables research and product teams to compare large language models, measure quality, cost, and latency, and make informed model selection and development decisions. The engineer owns the approach from early prototypes through reliable, maintainable tools, collaborating with research scientists, engineering teams, and product partners while helping shape technical direction and delivery.

Requirements

8–10+ years of relevant software engineering experience, including building and maintaining production systems involving machine learning or complex data.
Strong hands-on Python programming skills, including API design, automated testing, and working with structured and unstructured data.
Technical leadership and end-to-end delivery experience, including architecture decisions, engineering planning, staffing and effort estimation, project execution, and adapting priorities as requirements change.
Experience collaborating with research scientists or machine learning practitioners to translate experiments and research methods into working software.
Experience integrating or testing machine learning models, interpreting evaluation results, and applying findings to engineering decisions.
Understanding of language models and ability to recognize unreliable comparisons or test-data leakage.
Cloud engineering experience using AWS, Azure, or a comparable platform, including distributed workflows, automated deployment, and monitoring.
Working knowledge of SQL and experience developing software that processes structured and unstructured data.
Strong collaboration, communication, problem-solving, and mentoring skills, with experience coordinating technical delivery across research, engineering, and product stakeholders.
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Nice To Haves

Experience building LLM evaluation and benchmarking pipelines, including automated scoring methods and human assessment workflows.
Familiarity with experiment tracking, dataset versioning, and reproducible evaluation processes.
Experience in natural language processing, information retrieval, or other applications involving complex text.
Experience reducing model inference or operating costs while maintaining evaluation quality and system performance.
A track record of developing reusable software libraries, frameworks, or tools adopted by other engineering teams.
Research contributions or open-source software contributions.

Responsibilities

Partner with program and research leaders to define priorities, break projects into delivery stages, estimate effort and cost, recommend staffing and skills, identify dependencies, and adapt plans to available resources.
Extend shared tools and services, design new components, and develop production-quality Python software, APIs, and reusable evaluation frameworks.
Deliver initial versions, validate them with users, and improve them iteratively.
Collaborate with scientists to build automated evaluation pipelines connecting model APIs, test datasets, and scoring methods.
Implement reliable workflows for comparing AI models using evaluation datasets relevant to products and customer needs.
Maintain repeatable experiments, separate evaluation data from model development, and make results traceable to evaluation inputs and methods.
Measure model quality, operating cost, and latency to support product teams in validating model performance.
Develop user interfaces, documentation, evaluation reports, and diagnostic capabilities that help teams run evaluations, understand failures, interpret results, and extend tools independently.
Integrate automated testing, deployment, and monitoring; address performance, scalability, and operating costs; establish service maintenance and support ownership; and work with data specialists on access controls, permitted-use rules, and retention requirements.
Collaborate with research, data engineering, platform engineering, and product teams to align interfaces, responsibilities, and delivery plans.
Resolve technical dependencies, mentor engineers, communicate trade-offs and delivery risks, and recommend changes to scope or technical approach when necessary.

Benefits

Flexible hybrid work environment for office-based roles.
Work from anywhere for up to 8 weeks per year.
Flexible vacation and paid time off, including two company-wide mental health days.
Access to the Headspace app.
Retirement savings plan, including a competitive 401(k) with company match in the United States.
Tuition reimbursement.
Employee incentive programs and possible annual bonus based on enterprise and individual performance.
Health, dental, vision, disability, and life insurance programs.
Parental leave and sabbatical leave.
Paid volunteer days and opportunities to participate in pro-bono consulting and ESG initiatives.
Flexible Spending and Health Savings Accounts.
Fitness reimbursement and Employee Assistance Program.
Commuter benefits.
Adoption and surrogacy assistance.
Employee Stock Purchase Plan.
Optional hospital, accident and sickness insurance, optional life and AD&D insurance, and group legal and identity theft protection.
Access to a 529 Plan.