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Thomson Reuters

Legal, financial data, and media services

Lead Research Engineer - AI Evaluation & Benchmarking

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Eight to ten or more 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 the 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.
  • A bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Responsibilities

  • Lead technical delivery and engineering planning by partnering with program and research leaders to define priorities, break projects into delivery stages, estimate effort and cost, recommend staffing and required skills, identify dependencies, and adapt plans to available resources.
  • Design and build scalable evaluation infrastructure by extending shared tools and services, designing new components where needed, and developing 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 agreed scoring methods, and implement reliable workflows for comparing language models and other artificial intelligence 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 enable scientists and product teams to 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 to implement access controls, permitted-use rules, and retention requirements.
  • Partner with foundational 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.

Desired Qualifications

  • Experience building language model 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.

About the Company

Thomson Reuters

Legal, financial data, and media services

Thomson Reuters provides intelligent information and professional services for law, tax, finance, and media. It serves law firms, corporations, government agencies, and financial institutions with platforms like legal research (Westlaw), tax software (UltraTax CS), financial market data, and news services. These products are accessed through subscription-based platforms, ensuring ongoing updates and a steady revenue stream. The company also monetizes through advertising on media platforms and data analytics sales. It distinguishes itself by offering a broad, integrated suite of industry-specific tools combined with AI, cognitive computing, and machine learning to improve accuracy, efficiency, and predictive analytics in research and data insights. Its goal is to be a trusted partner that helps professionals make informed decisions by delivering reliable information and decision-ready tools while pursuing responsible, values-driven initiatives.

Company size
10,001+
Company stage
IPO
Headquarters
Toronto, Canada
Founded
1851

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