Senior Machine Learning Engineer – NLP/LLM

Thomson Reuters•Brooklyn, NY

2d•Hybrid

About The Position

As a Senior Machine Learning Engineer, you will build and deploy production machine learning and large language model (LLM) systems that extract actionable insights from complex legal documents and data. You will work on challenging natural language processing problems involving contractual language, information extraction, model-driven analysis, and comparisons across large collections of documents. You will join a highly technical machine learning team and collaborate with machine learning engineers, legal subject matter experts, product engineers, and data and security partners. This is a hands-on role for someone who has personally built, trained, evaluated, and deployed machine learning models into production and wants to apply that expertise to sophisticated real-world products. This role may be remote within the United States or hybrid from our New York City office.

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.

Nice To Haves

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.

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.

Benefits

Flexible vacation
Two company-wide Mental Health Days off
Access to the Headspace app
Retirement savings
Tuition reimbursement
Employee incentive programs
Resources for mental, physical, and financial wellbeing
Flexible hybrid working environment
Work from anywhere for up to 8 weeks per year
Paid volunteer days off annually
Opportunities to get involved with pro-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives
Market competitive health, dental, vision, disability, and life insurance programs
Competitive 401k plan with company match
Competitive vacation, sick and safe paid time off
Paid holidays
Parental leave
Sabbatical leave
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 employee
Access to 529 Plan
Commuter benefits
Adoption & Surrogacy Assistance
Access to Employee Stock Purchase Plan