Jobs at Thomson Reuters

Leads the design, development, evaluation, and production deployment of document understanding systems for legal, tax, and accounting content. Responsibilities include semantic chunking, document enrichment, hierarchical classification, entity and citation extraction, knowledge graph construction, LLM post-training, model distillation, synthetic data generation, and evaluation framework development. The role guides architecture and AI strategy, partners with engineering on scalable delivery, influences leadership, and mentors applied scientists and ML practitioners.

Architects and ships production AI agent systems, including multi-component pipelines, tool integrations, orchestration infrastructure, observability, evaluations, and reliability practices. The role owns complex systems end-to-end, contributes to technical architecture and progressive delivery, collaborates across product and engineering, and mentors engineers. It requires strong backend, distributed systems, cloud infrastructure, LLM, and agentic development experience.

Build, train, evaluate, and deploy production machine learning and LLM systems for legal document intelligence. Develop NLP solutions for information extraction, summarization, search, AI agents, and document analysis. Create scalable ML pipelines, evaluation frameworks, and reliability metrics while optimizing model performance. Collaborate with engineering, product, legal, data, and security teams to deliver secure AI capabilities and translate business problems into production solutions.

Design and lead enterprise AI systems using generative AI, RAG, knowledge graphs, and autonomous agents. Define technical strategy, evaluate emerging AI technologies, and guide architecture and roadmaps. Mentor engineers, champion MLOps and LLMOps governance, and ensure security, testing, monitoring, and observability. Collaborate with research, engineering, design, and product teams to deliver scalable customer-facing solutions while optimizing latency, cost, and user experience.

Designs and leads enterprise AI systems for legal research, including RAG pipelines, knowledge graphs, autonomous agents, and AI platforms. Provides technical strategy, architecture, mentoring, governance, testing, observability, and operational leadership. Partners with research, engineering, design, and product teams to launch scalable, secure AI products. Requires hands-on software engineering expertise in generative AI, cloud-native systems, APIs, distributed systems, and production AI deployments.

Own the SafeSend product portfolio and lead its evolution into an AI-driven Gather & Deliver experience. Responsibilities include converging SafeSend and TaxCaddy, developing reusable workflow components, expanding APIs and integrations, enabling agent orchestration through CoCounsel, and delivering reliable tax-season performance. The role requires platform product leadership, customer migration expertise, AI product fluency, systems thinking, stakeholder management, and team-building skills.

Own the vision, strategy, roadmap, commercialization, and lifecycle of complex analytical products. Conduct market and competitive research, translate customer needs into requirements, collaborate with engineering, design, sales, and marketing, and lead go-to-market activities. Monitor adoption and performance, gather feedback, prioritize improvements, communicate with stakeholders, and guide AI-enabled product development. The role also coordinates cross-functional initiatives and balances strategic planning with tactical execution.

Owns vision, strategy, priorities, and lifecycle outcomes for a complex product or product portfolio. Partners with design and engineering to shape roadmaps, validate ideas, manage trade-offs, and deliver high-quality, compliant solutions. Uses customer, market, technology, and commercial insights to guide investment decisions. Leads cross-functional initiatives, develops junior Product Managers, and drives AI-enabled product experiences without direct people-management responsibility.

Leads the design, development, evaluation, and production deployment of large-scale search and information retrieval systems. Responsibilities include indexing and ingestion pipelines, traditional and LLM-based ranking, semantic and hybrid retrieval, query understanding, relevance evaluation, search infrastructure, and retrieval optimization. The role partners with engineering and product teams, advises leadership on search and AI strategy, develops self-service search platforms, publishes research, and mentors applied scientists and machine learning practitioners.

Provides technical leadership for cloud cost management and reporting platforms. Designs scalable software, APIs, data pipelines, and analytics solutions; establishes engineering standards; leads modernization, automation, security, observability, and CI/CD practices; and drives cross-functional initiatives. The role partners with product, FinOps, cloud platform, and business teams, mentors engineers, leads design reviews, and transforms cloud and AI cost data into actionable insights.

Creates and maintains authoritative tax advisory and compliance content; monitors legislation and Canada Revenue Agency guidance; analyzes and communicates complex tax developments; manages expert contributors and quality standards; collaborates on AI and tax-advisory initiatives; validates tax technology solutions; and provides feedback on AI performance. Requires deep tax expertise, strong writing and editing skills, bilingual French/English communication, technical adaptability, and at least seven years of tax advisory and compliance experience.

Design, build, and deploy production-grade legal AI solutions by translating legal user needs into robust software. Collaborate with AI engineers, apply prompt/LLM evaluation, use DevOps and Infrastructure-as-Code, practice TDD, mentor junior engineers, lead agile projects, and deliver end-to-end solutions for law firms and legal departments.

Sets technical direction for APIs that provide governed access to legal content for AI agents. Owns API architecture, retrieval and search design, MCP integrations, evaluation frameworks, reliability, observability, security, and platform governance. Leads cross-team architecture decisions, mentors engineers, manages operational responsibilities, and establishes AI-assisted, spec-driven development practices for a multi-team internal platform.

Lead product strategy and roadmap to enter Financial Services for Noetica. Define customer requirements, design data-intensive features, partner with engineering, GTM and sales, and drive commercial growth and integrations to scale the product for fund managers, credit managers, and treasuries.

The Lead Data Engineer will architect and develop reporting solutions, integrate AI, analyze complex datasets, and mentor team members to enhance data-driven decision-making.

Build and maintain predictive models, including propensity-to-upgrade and engagement scoring, to drive AI product adoption and customer expansion. Develop features from product, behavioral, campaign, learning, and firmographic data. Partner with Marketing, Customer Success, Data Engineering, Product, and Commercial teams to operationalize models in Treasure Data and activation workflows. Monitor model performance, support experimentation, measure lift, document methodologies, and communicate insights to technical and nontechnical stakeholders.

Drive new business acquisition across the Canadian corporate legal market by prospecting, managing complex sales cycles, conducting consultative presentations, closing opportunities, and achieving revenue targets. Build executive relationships, maintain pipeline and CRM records, develop territory strategies, monitor market trends, attend industry events, and collaborate with marketing, product, and customer success teams.

Build and maintain a scalable cloud-native document-processing platform supporting AI products and millions of documents monthly. Design client-facing REST APIs, develop services using C#/.NET and SQL, and work with containers, Kubernetes, CI/CD, and cloud infrastructure. Contribute to architecture and technical decisions, mentor junior engineers, collaborate cross-functionally, and participate in on-call support. Experience with AI/ML, LLM applications, document processing, and MLOps is highly desirable.

As a Senior Software Engineer, Full Stack, you will build backend services and APIs for a document analysis platform, focusing on server-side architecture and data processing, while also contributing to frontend features as needed.

Build and deliver production backend, platform, and AI capabilities using C#/.NET and Python. Develop distributed systems, LLM applications, AI agents, retrieval workflows, MCP servers, document understanding, and scalable AI infrastructure. Provide hands-on Staff-level technical leadership through architecture, coding, technical direction, mentoring, deployment, reliability, safety controls, and production operations.