T
Thomson Reuters
Senior Machine Learning Engineer (NLP/LLM)
Skills
PythonPyTorchTensorFlowMachine LearningAIWorkdayNLP / Natural Language Processing
17 days ago
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Important information
Contract type:
Permanent
Salary:
$110,000 – $235,000 / year
Location:
New York, NY, USA
Work mode:
Onsite
Published on:
7 October 2026
What they need
- 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
- 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- Experience with at least one of the following: information extraction, text generation/summarization, AI agents, or search
- 3+ years of professional machine learning engineering, applied machine learning, research engineering, or closely related software engineering experience
- Experience designing and applying model evaluation methods and metrics to measure the performance and reliability of production machine learning systems
- Advanced Python programming skills and hands-on experience with machine learning frameworks such as PyTorch or TensorFlow
- 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
- Experience developing or fine-tuning language models or other machine learning models for specialized use cases or domains
- Ability to translate product or business problems into machine learning solutions and clearly communicate technical decisions, trade-offs, and outcomes
- Master’s degree in Machine Learning, Computer Science, Statistics, or a closely related quantitative field with a focus on machine learning or artificial intelligence
- Strong problem-solving, collaboration, and ownership skills, with the ability to work effectively across technical and domain-focused teams
- Strong practical experience with machine learning, natural language processing, and modern LLM architectures
- PhD in Machine Learning, Computer Science, Statistics, or a closely related quantitative field
- Experience with legal technology, legal natural language processing, legal document analysis, or other domain-specific language modeling
- Experience deploying and operating ML or LLM systems at scale in production environments
- 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- End Date: December 25, 2026
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