Research Engineer

• Build & Deploy Product Analytics Infrastructure: Design and implement scalable data pipelines and analytics systems that transform customer feedback and product traces into actionable insights across AI-powered legal products.
• Enable AI Evaluation at Scale: Build data workflows to enable the deployment of automated evaluation metrics as production analytics to continuously track product quality, detect errors, and alert teams to regressions before they impact customers.
• Establish Data Governance & Quality Standards: Develop technical governance infrastructure for manual and automated review of AI product data, particularly for small and medium law firms, ensuring data quality, security, and compliance.
• Drive Metric Development: Analyze product traces and customer feedback to identify quality issues and patterns that inform the development of new evaluation metrics and feed into product roadmap decisions.
• Support Cross-Functional Teams: Partner closely with Product Scientists, Research Engineers, and Subject Matter Experts to implement configurations, build reporting dashboards, and create self-service tools for metric implementation.
• Advance AI Evaluation Best Practices: Contribute to the development and scaling of automated evaluation capabilities and establish best practices for AI evaluation and analytics across Thomson Reuters pillars (Legal, Tax & Accounting, and Reuters News).

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Research Engineer – Data & Analytics, ML-Enabled Systems