Senior Machine Learning Engineer – NLP, LLM

Thomson Reuters

. Build and deploy production machine learning and large language model systems extracting actionable insights from complex legal documents and data .

Posted 10/3/2026full-timeUnited StatesSenior💰 $110,000 - $235,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying machine learning and large language model systems, with a strong focus on natural language processing and information extraction from complex legal documents. Proficient in optimizing model performance and translating business problems into actionable machine learning solutions.

Highest-signal resume keywords

Machine Learning EngineeringNatural Language ProcessingPython ProgrammingPyTorchTensorFlow

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningInformation ExtractionText GenerationModel EvaluationFeature EngineeringModel TuningDocument AnalysisLarge Language ModelsAI AgentsStatistical Analysis

Soft Skills

Problem-SolvingCollaborationOwnershipCommunication

Certifications & Qualifications

Master’s Degree in Machine LearningMaster’s Degree in Computer ScienceMaster’s Degree in Statistics

Industry Keywords

Legal TechnologyLegal NLPDomain-Specific Language ModelingFinancial ServicesRegulated Industries

Tech Stack

Tools & technologies

PythonPyTorchTensorflow

About the role

Key responsibilities & impact

Build and deploy production machine learning and large language model systems extracting actionable insights from complex legal documents and data
Solve natural language processing problems involving contractual language, information extraction, model-driven analysis, and document comparisons
Design, build, train, and deploy machine learning and LLM-based models and systems
Develop production solutions for information extraction, text generation and summarization, AI agents, search, and document analysis
Build scalable and reliable machine learning pipelines for training, evaluation, deployment, and ongoing production use
Develop model evaluation frameworks and metrics for 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 machine learning solutions from experimentation through scaled production deployment
Collaborate with machine learning, engineering, product, legal, data, and security teams
Protect sensitive information while delivering reliable AI capabilities

Requirements

What you’ll need

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
Strong practical experience with machine learning, natural language processing, and modern LLM architectures
Experience with information extraction, text generation/summarization, AI agents, or search
Advanced Python programming skills
Hands-on experience with 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
Ability to translate product or business problems into machine learning solutions and communicate technical decisions, trade-offs, and outcomes
Strong problem-solving, collaboration, and ownership skills
Preferred: PhD in Machine Learning, Computer Science, Statistics, or a closely related quantitative field
Preferred: Experience deploying and operating ML or LLM systems at scale
Preferred: Experience with legal technology, legal NLP, legal document analysis, or domain-specific language modeling
Preferred: Experience in financial services, economics, or regulated/data-sensitive industries
Preferred: Experience serving or self-hosting LLMs and optimizing computational efficiency
Preferred: Experience taking complex ML initiatives from experimentation or research through production and demonstrating measurable impact

Benefits

Comp & perks

Flexible hybrid working environment for office-based roles
Work from anywhere for up to 8 weeks per year
Flexible vacation
Two company-wide Mental Health Days off
Headspace app access
Retirement savings
Tuition reimbursement
Employee incentive programs
Resources for mental, physical, and financial wellbeing
Two paid volunteer days off annually
Pro-bono consulting project opportunities
Health, dental, vision, disability, and life insurance programs
401(k) plan with company match
Sick and safe paid time off
Paid holidays, including two company mental health days off
Parental leave
Sabbatical leave
Optional hospital, accident and sickness insurance
Optional life and AD&D insurance
Flexible Spending and Health Savings Accounts
Fitness reimbursement
Employee Assistance Program
Group Legal Identity Theft Protection benefit
529 Plan access
Commuter benefits
Adoption & Surrogacy Assistance
Employee Stock Purchase Plan
May be eligible for an Annual Bonus based on enterprise and individual performance