Senior Machine Learning Engineer – NLP/LLM

Thomson Reuters•Frisco, TX

9d•$110,000 - $235,000

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. The work involves natural language processing problems such as contractual language, information extraction, model-driven analysis, and comparisons across large document collections. You will join a technical machine learning team and collaborate with machine learning engineers, legal subject matter experts, product engineers, and data and security partners. This hands-on role is intended for someone with personal experience building, training, evaluating, and deploying machine learning models into production. The role may be remote within the United States or hybrid from the New York City office. The posting is for proactive recruitment and may be used to fill current openings or future vacancies.

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.
At least 5 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 model development through production deployment.
Strong practical experience with machine learning, natural language processing, and modern LLM architectures.
Experience with at least one of information extraction, text generation or 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 machine learning 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 for natural language processing and document intelligence problems.
Develop production solutions for information extraction, text generation and summarization, AI agents, search, and document analysis.
Build scalable and reliable machine learning pipelines for model training, evaluation, deployment, and ongoing production use.
Develop 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 business and product problems into practical machine learning solutions and take them 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 and flexible work arrangements, including work from anywhere for up to 8 weeks per year.
Two company-wide Mental Health Days off annually.
Access to the Headspace app.
Retirement savings and a competitive 401(k) plan with company match.
Tuition reimbursement.
Employee incentive programs and an annual bonus may be available based on enterprise and individual performance.
Resources for mental, physical, and financial wellbeing, including an Employee Assistance Program and fitness reimbursement.
Health, dental, vision, disability, and life insurance.
Paid holidays, sick and safe paid time off, parental leave, and sabbatical leave.
Flexible Spending and Health Savings Accounts.
Commuter benefits.
Adoption and surrogacy assistance.
Employee Stock Purchase Plan.
Two paid volunteer days off annually and opportunities to participate in pro-bono consulting projects and ESG initiatives.
Optional hospital, accident and sickness insurance; optional life and AD&D insurance; and Group Legal Identity Theft Protection benefit, paid 100% by the employee.
Access to a 529 Plan.