Senior Machine Learning Engineer – NLP/LLM

Thomson Reuters is seeking a Senior Machine Learning Engineer to build and deploy production machine learning and large language model (LLM) systems that extract actionable insights from complex legal documents and data. This role focuses on natural language processing for contractual language, information extraction, model-driven analysis, and document comparisons.

Location and Work Arrangement

• Location: Brooklyn, NY (hybrid)
• Alternative arrangement: Remote within the United States or hybrid from the company’s New York City office

Salary

• Annual base compensation: USD 100,000 - 235,000

Note on base compensation ranges by eligible location: The base compensation range for the role varies by office location, with $127,000 USD - $235,000 USD for certain eligible locations and $110,000 USD - $204,200 USD for other eligible US locations. For Ontario, Canada, the base compensation range is $100,000 CAD - $145,000 CAD.

Job Responsibilities

• Design, build, train, and deploy machine learning and LLM-based models and systems for natural language processing and document intelligence.
• Develop production solutions for information extraction, text generation and summarization, AI agents, search, and document analysis.
• Build scalable and reliable machine learning pipelines to support model training, evaluation, deployment, and ongoing production use.
• Create 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 drive them from experimentation through large-scale production deployment.
• Collaborate across machine learning, engineering, product, legal domain, data, and security teams to deliver reliable AI capabilities while protecting sensitive information.

Required Qualifications

• 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.
• Hands-on experience building, training, and deploying machine learning models into production, including explaining individual contributions from development through deployment.
• Strong practical experience with machine learning, natural language processing, and modern LLM architectures.
• Experience with at least one of: 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 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 across technical and domain-focused teams.

Technology Stack

• Python
• PyTorch
• TensorFlow
• LLM architectures
• Large language model (LLM)

Benefits

• Hybrid Work Model
• Flexibility & Work-Life Balance (Flex My Way; work from anywhere for up to 8 weeks per year)
• Career Development and Growth (Grow My Way programming and skills-first approach)
• Industry competitive benefits including flexible vacation, two company-wide Mental Health Days off, Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for wellbeing
• Social Impact Institute including two paid volunteer days off annually, pro-bono consulting projects, and ESG initiatives
• Comprehensive benefits package in the United States including market-competitive health, dental, vision, disability, and life insurance programs
• Competitive 401k plan with company match
• Parental leave; sabbatical leave; paid holidays (including two company mental health days off); paid time off
• Optional hospital, accident and sickness insurance paid 100% by the employee
• Optional life and AD&D insurance paid 100% by the employee
• Flexible Spending and Health Savings Accounts
• Fitness reimbursement
• Access to Employee Assistance Program
• Group Legal Identity Theft Protection benefit paid 100% by the employee
• Access to 529 Plan; commuter benefits; Adoption & Surrogacy Assistance; Tuition Reimbursement; access to Employee Stock Purchase Plan
• Annual Bonus eligibility based on a combination of enterprise and individual performance

Preferred Qualifications

• 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, including demonstrating measurable product or business impact.

Eligible Office Locations

• New York City, San Francisco, Los Angeles, and/or Irvine, CA; McLean, VA; Washington, DC

Thomson Reuters Use of AI in Recruitment

Thomson Reuters uses Artificial Intelligence (AI) to support parts of the global recruitment process. Unless you opt out, the AI system will assess information you provide, compare it to the role requirements, and share results with recruitment personnel for further review. The AI system supports the process, but a human reviewer makes the final decision on whether you are considered for the role.