Thomson Reuters is seeking a Senior Machine Learning Engineer to design, build, train, and deploy production machine learning and LLM systems focused on extracting actionable insights from complex legal documents and data. The role centers on natural language processing across contractual language, information extraction, model-driven analysis, and document comparisons.
Role Overview
You will build and deploy machine learning and large language model (LLM) systems that support document intelligence use cases, including information extraction, text generation and summarization, AI agents, search, and broader document analysis. The position also includes developing scalable pipelines for model training, evaluation, and reliable ongoing production use.
Key Responsibilities
- Design, build, train, and deploy machine learning and LLM-based models and systems for complex natural language processing and document intelligence problems.
- Develop production solutions across information extraction, text generation and summarization, AI agents, search, and document analysis.
- Build scalable, reliable machine learning pipelines supporting training, evaluation, deployment, and continued production operation.
- Create model evaluation frameworks and metrics to assess quality, accuracy, reliability, drift, and potential bias.
- Optimize model performance and resource utilization through experimentation, feature engineering, model selection, and tuning.
- Translate product and business problems into 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 dependable 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 the ability to explain your individual contribution from model development through deployment.
- Practical experience with machine learning, natural language processing, and modern LLM architectures.
- Experience with at least one of: information extraction, text generation and summarization, AI agents, or search.
- Advanced Python 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 evaluation methods and metrics to measure performance and reliability in production systems.
- Ability to translate product or business problems into machine learning solutions, and communicate technical decisions, trade-offs, and outcomes clearly.
- Strong problem-solving, collaboration, and ownership skills, with effectiveness across technical and domain-focused teams.
Technologies
- Python
- PyTorch
- TensorFlow
- LLM architectures
- Large language model (LLM)
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, demonstrating measurable product or business impact.
Work Location and Arrangement
- Brooklyn, NY (hybrid)
- Remote within the United States or hybrid from the New York City office
Compensation
- Salary range: USD 100,000 - 235,000 per year
- Base compensation range varies by eligible office location:
- For locations listed as eligible US locations, unless otherwise noted: $110,000 USD - $204,200 USD
- For eligible office locations including New York City, San Francisco, Los Angeles, and Irvine (CA), McLean (VA), and Washington (DC): $127,000 USD - $235,000 USD
- For Ontario, Canada: $100,000 CAD - $145,000 CAD
Benefits
- Hybrid work model
- Flex My Way flexibility for work from anywhere for up to 8 weeks per year, supporting work-life balance
- Grow My Way programming and a skills-first approach for career development and growth
- Industry competitive benefits including flexible vacation, two company-wide Mental Health Days off, Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and wellbeing resources
- Social Impact Institute with 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), and 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, and access to the Employee Stock Purchase Plan
- Annual bonus eligibility based on enterprise and individual performance
AI in the Recruitment Process
Thomson Reuters uses Artificial Intelligence (AI) to support parts of the global recruitment process. Unless you opt out, the AI system assesses the information you provide, compares it to the role requirements, and shares the result with recruitment personnel for further review. A human recruiter makes the final decision regarding your consideration.
