Back to jobs

Senior Machine Learning Engineer – NLP/LLM

United States of America, Brooklyn, New York

Live, first seen by us 32 days ago
Pay
Multiple pay amounts — pay source
In the United States, Thomson Reuters offers a comprehensive benefits package to our employees. Our benefit package includes market competitive health, dental, vision, disability, and life insurance programs, as well as a competitive 401k plan with company match. In addition, Thomson Reuters offers market leading work life benefits with competitive vacation, sick and safe paid time off, paid holidays (including two company mental health days off), parental leave, sabbatical leave. These benefits meet or exceeds the requirements of paid time off in accordance with any applicable state or municipal laws. Finally, Thomson Reuters offers the following additional benefits: 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; Tuition Reimbursement; and access to Employee Stock Purchase Plan. Thomson Reuters complies with local laws that require upfront disclosure of the expected pay range for a position. The base compensation range varies across locations.

Eligible office location(s) for this role include one or more of the following: New York City, San Francisco, Los Angeles, and/or Irvine, CA; McLean, VA; Washington, DC. The base compensation range for the role in any of those locations is $127,000 USD - $235,000 USD.
For any eligible US locations, unless otherwise noted, the base compensation range for this role is $110,000 USD - $204,200 USD.
For Ontario, Canada, the base compensation range for this role is $100,000 CAD - $145,000 CAD.

Base pay is positioned within the range based on several factors including an individual’s knowledge, skills and experience with consideration given to internal equity. Base pay is one part of a comprehensive Total Reward program which also includes flexible and supportive benefits and other wellbeing programs.
This role may also be eligible for an Annual Bonus based on a combination of enterprise and individual performance.
 About Us
Read the full posting
Work setting
Not stated
Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Design, build, train, and deploy ML and LLM models for NLP and document intelligence

  • Build scalable ML pipelines for training, evaluation, deployment, and production use

  • Develop model evaluation frameworks covering quality, reliability, drift, and bias

  • Optimize model performance through experimentation, feature engineering, and tuning

  • Take solutions from experimentation through production deployment at scale

From the employer’s posting
Design, build, train, and deploy machine learning and LLM-based models and systems that solve complex natural language processing and document intelligence problems.
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.
take those solutions from experimentation through production deployment at scale

What you’ll bring

All qualifications

Core experience

  • Master’s degree in Machine Learning, Computer Science, Statistics, or a closely related quantitative field with a focus on machine learning or artificial intelligence.
  • 5+ years of professional machine learning engineering, applied machine learning, research engineering, or closely related software engineering experience.
  • Experience with at least one of the following: information extraction, text generation/summarization, AI agents, or search.
  • 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.

Preferred experience

  • 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.
Qualification wording
Master’s degree in Machine Learning, Computer Science, Statistics, or a closely related quantitative field with a focus on machine learning or artificial intelligence.
5+ years of professional machine learning engineering, applied machine learning, research engineering, or closely related software engineering experience.
Experience with at least one of the following: information extraction, text generation/summarization, AI agents, or search.
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.
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.
Education & alternatives
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. - 5+ years of professional machine learning engineering, applied machine learning, research engineering, or closely related software engineering experience.
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.

About Thomson Reuters

We serve professionals across legal, tax, accounting, compliance, government, and media.

In the employer’s words · Read in context

Apply at Thomson Reuters

Posting record

Report an error
Status in our records
Live
First seen by us
Oct 3, 202632 days ago
Recorded sightings
87
Last seen by us
Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$166K–$240K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 1,279 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 32

Similar jobs usually come down between day 6 and day 69.

From 4,296 similar postings that came down. Coming down is not always a hire.

Thomson Reuters is hiring

19 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Hiring has slowed
  • Takes jobs down at a usual pace
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • PyTorch
  • TensorFlow
Where the posting names them
- 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.

Your next step

Complete your application on thomsonreuters.wd5.myworkdayjobs.com. The employer’s form will show what is required.

Applying through Workday

  • You create an account with each employer before you can upload anything. Every Workday employer has its own account, so one sign-up doesn’t carry over.
  • “Autofill with Resume” comes right after sign-in, before the My Information and My Experience steps. Check every field it fills in those steps, since that’s what recruiters see.
  • Your application status lives in that employer’s Candidate Home, under the same sign-in.

Already applied? Track this application

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

Job Description

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.


Key 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.

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.
  • 5+ 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.

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 and demonstrating measurable product or business impact.

#LI-TH1

 

 

This posting is for proactive recruitment purposes and may be used to fill current openings or future vacancies within our organization.

What’s in it For You?

  • Hybrid Work Model: We’ve adopted a flexible hybrid working environment for our office-based roles while delivering a seamless experience that is digitally and physically connected.
  • Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance.
  • Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow’s challenges and deliver real-world solutions. Our Grow My Way programming and skills-first approach ensures you have the tools and knowledge to grow, lead, and thrive in an AI-enabled future.
  • Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing.
  • Culture: Globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more. We live by our values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together.
  • Social Impact: Make an impact in your community with our Social Impact Institute. We offer employees two paid volunteer days off annually and opportunities to get involved with pro-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives.
  • Making a Real-World Impact: We are one of the few companies globally that helps its customers pursue justice, truth, and transparency. Together, with the professionals and institutions we serve, we help uphold the rule of law, turn the wheels of commerce, catch bad actors, report the facts, and provide trusted, unbiased information to people all over the world.

Our use of AI within the recruitment process Thomson Reuters utilizes Artificial Intelligence (AI) to support parts of our global recruitment process. Unless you opt-out, our AI system will assess the information provided by you and compare it to the requirements listed for the role, and present the result to our recruitment personnel for further review. The AI system acts as a supporting tool, but there is always a human making the decision if you will be considered for the role.

 

 

In the United States, Thomson Reuters offers a comprehensive benefits package to our employees. Our benefit package includes market competitive health, dental, vision, disability, and life insurance programs, as well as a competitive 401k plan with company match. In addition, Thomson Reuters offers market leading work life benefits with competitive vacation, sick and safe paid time off, paid holidays (including two company mental health days off), parental leave, sabbatical leave. These benefits meet or exceeds the requirements of paid time off in accordance with any applicable state or municipal laws. Finally, Thomson Reuters offers the following additional benefits: 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; Tuition Reimbursement; and access to Employee Stock Purchase Plan.

Thomson Reuters complies with local laws that require upfront disclosure of the expected pay range for a position. The base compensation range varies across locations.

Eligible office location(s) for this role include one or more of the following: New York City, San Francisco, Los Angeles, and/or Irvine, CA; McLean, VA; Washington, DC. The base compensation range for the role in any of those locations is $127,000 USD - $235,000 USD.
For any eligible US locations, unless otherwise noted, the base compensation range for this role is $110,000 USD - $204,200 USD.
For Ontario, Canada, the base compensation range for this role is $100,000 CAD - $145,000 CAD.

Base pay is positioned within the range based on several factors including an individual’s knowledge, skills and experience with consideration given to internal equity. Base pay is one part of a comprehensive Total Reward program which also includes flexible and supportive benefits and other wellbeing programs.
This role may also be eligible for an Annual Bonus based on a combination of enterprise and individual performance.


About Us

Thomson Reuters informs the way forward by bringing together the trusted content and technology that people and organizations need to make the right decisions. We serve professionals across legal, tax, accounting, compliance, government, and media. Our products combine highly specialized software and insights to empower professionals with the data, intelligence, and solutions needed to make informed decisions, and to help institutions in their pursuit of justice, truth, and transparency. Reuters, part of Thomson Reuters, is a world leading provider of trusted journalism and news.

We are powered by the talents of 26,000 employees across more than 70 countries, where everyone has a chance to contribute and grow professionally in flexible work environments. At a time when objectivity, accuracy, fairness, and transparency are under attack, we consider it our duty to pursue them. Sound exciting? Join us and help shape the industries that move society forward.

As a global business, we rely on the unique backgrounds, perspectives, and experiences of all employees to deliver on our business goals. To ensure we can do that, we seek talented, qualified employees in all our operations around the world regardless of race, color, sex/gender, including pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law. Thomson Reuters is proud to be an Equal Employment Opportunity Employer providing a drug-free workplace.

Thomson Reuters makes reasonable accommodations for applicants with disabilities, including veterans with disabilities, and for sincerely held religious beliefs in accordance with applicable law. If you reside in the United States and require an accommodation in the recruiting process, you may contact our Human Resources Department at HR.Leave-Expert@thomsonreuters.com. Disability accommodations in the recruiting process may include things like a sign language interpreter, making interview rooms accessible, providing assistive technology, or other relevant accommodations. Please note this email is not intended for general recruitment questions and we will promptly respond to inquiries regarding accommodations. More information on requesting an accommodation here.

Learn more on how to protect yourself from fraudulent job postings here.

More information about Thomson Reuters can be found on thomsonreuters.com

Source details

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay

This passage needs a closer read in the full description.

Location & working pattern

United States of America, Brooklyn, New York

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. Key Responsibilities
More source context
This posting is for proactive recruitment purposes and may be used to fill current openings or future vacancies within our organization. What’s in it For You? - Hybrid Work Model: We’ve adopted a flexible hybrid working environment for our office-based roles while delivering a seamless experience that is digitally and physically connected. - Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance.
Work authorization

This passage needs a closer read in the full description.

Find answers in the posting

AI
How answers work

AI selects complete passages from this posting. Check them for conditions and exceptions.

Uses this posting and your question. No profile needed.