Senior Machine Learning Engineer – NLP/LLM

Thomson Reuters provides trusted content and technology that help professionals and organizations make informed decisions across legal and other specialized industries. The Senior Machine Learning Engineer will build, evaluate, and deploy production machine learning and large language model systems for natural language processing and legal document intelligence. The role involves developing scalable ML pipelines, optimizing models, and collaborating across engineering, product, legal, data, and security teams.

Responsibilities:

• Design, build, train, and deploy machine learning and LLM-based models and systems that solve complex natural language processing and document intelligence problems
• Develop production solutions for areas such as information extraction, text generation and summarization, AI agents, search, and document analysis
• Build scalable and reliable machine learning pipelines that support model training, evaluation, deployment, and ongoing production use
• Develop rigorous model evaluation frameworks and metrics to assess model quality, accuracy, reliability, drift, and potential bias
• Optimize model performance and resource utilization through experimentation, feature engineering, model selection, and tuning
• Translate complex business and product problems into practical machine learning solutions and take those solutions from experimentation through production deployment at scale
• Collaborate across machine learning, engineering, product, legal domain, data, and security teams to deliver reliable AI capabilities while protecting sensitive information

Requirements:

• Master's degree in Machine Learning, Computer Science, Statistics, or a closely related quantitative field with a focus on machine learning or artificial intelligence
• 3+ years of professional machine learning engineering, applied machine learning, research engineering, or closely related software engineering experience
• Demonstrated hands-on experience building, training, and deploying machine learning models into production, including the ability to explain your individual contribution from model development through production deployment
• Strong practical experience with machine learning, natural language processing, and modern LLM architectures
• Experience with at least one of the following: information extraction, text generation/summarization, AI agents, or search
• Advanced Python programming skills and hands-on experience with machine learning frameworks such as PyTorch or TensorFlow
• Experience developing or fine-tuning language models or other machine learning models for specialized use cases or domains
• Experience designing and applying model evaluation methods and metrics to measure the performance and reliability of production machine learning systems
• Ability to translate product or business problems into machine learning solutions and clearly communicate technical decisions, trade-offs, and outcomes
• Strong problem-solving, collaboration, and ownership skills, with the ability to work effectively across technical and domain-focused teams
• PhD in Machine Learning, Computer Science, Statistics, or a closely related quantitative field
• Experience deploying and operating ML or LLM systems at scale in production environments
• Experience with legal technology, legal natural language processing, legal document analysis, or other domain-specific language modeling
• Experience applying machine learning within financial services, economics, or other regulated or data-sensitive industries
• Experience serving or self-hosting large language models and optimizing model performance and computational efficiency
• Experience taking complex ML initiatives from experimentation or research through production and demonstrating measurable product or business impact