Design evaluation frameworks - component-level and end-to-end - using expert annotation and ... text * LLM-based information extraction, few-shot and multi-task learning, and post-training
Design evaluation frameworks - component-level and end-to-end - using expert annotation and ... text * LLM-based information extraction, few-shot and multi-task learning, and post-training
Design evaluation frameworks - component-level and end-to-end - using expert annotation and ... text * LLM-based information extraction, few-shot and multi-task learning, and post-training
Posted today
Design evaluation frameworks - component-level and end-to-end - using expert annotation and ... text * LLM-based information extraction, few-shot and multi-task learning, and post-training
Posted today
... text * LLM-based information extraction, few-shot and multi-task learning, and post-training ... Annotation workflow design and evaluation framework development for document understanding tasks ...
... text * LLM-based information extraction, few-shot and multi-task learning, and post-training ... Annotation workflow design and evaluation framework development for document understanding tasks ...
Text Annotation information
What are typical day-to-day responsibilities for someone working in text annotation?
Text Annotation professionals spend much of their day reading and labeling text data according to specific guidelines, ensuring that information is correctly categorized and flagged. This can involve highlighting entities, identifying sentiments, tagging parts of speech, or annotating complex relationships within text documents. They frequently collaborate with project managers, data scientists, and quality assurance teams to clarify instructions and maintain data consistency. The role often involves independent work, but regular check-ins and feedback sessions help maintain accuracy and enhance understanding of evolving annotation requirements. This combination of independent and collaborative tasks makes the position dynamic and integral to successful AI or NLP project outcomes.
Are data annotations still hiring?
Data annotation roles, including those for text annotation, are still in demand as companies continue to develop AI and machine learning models. These jobs often require attention to detail and familiarity with annotation tools, and they can be available as remote or part-time positions. Hiring trends depend on industry needs and project pipelines, but opportunities remain consistent in this field.
What is a text annotation job?
A text annotation job involves labeling or tagging parts of text data to help train machine learning models, especially in natural language processing tasks. Workers typically review text and add labels such as entities, sentiments, or categories using specialized tools, often working remotely with flexible schedules.
What are the key skills and qualifications needed to thrive in the Text Annotation position, and why are they important?
Strong language proficiency, attention to detail, and critical thinking are essential skills for succeeding as a Text Annotation specialist, often supported by a bachelor's degree in linguistics, computer science, or a related field. Familiarity with annotation tools like Labelbox, Prodigy, or the Amazon Mechanical Turk platform, as well as knowledge of data privacy and handling protocols, is typically required. Excellent communication, self-motivation, and the ability to focus on repetitive tasks help individuals excel in this position. These capabilities ensure high-quality, consistent data labeling for machine learning models, supporting the development of cutting-edge AI solutions.
Is data annotation a legit job?
Data annotation is a legitimate job that involves labeling data such as images, text, or audio to help train machine learning models. It often requires attention to detail and familiarity with annotation tools, and it can be performed remotely or in-office. Many companies hire data annotators as part of their AI development teams.
What is a Text Annotation job?
A Text Annotation job involves labeling and categorizing text data to help train machine learning models. Annotators add tags, metadata, or classifications to text, enabling AI systems to understand language patterns. This work is essential for applications like chatbots, search engines, and sentiment analysis. Strong attention to detail and language proficiency are key skills for this role.
What qualifications do you need to be a data annotator?
To be a data annotator, basic qualifications typically include a high school diploma or equivalent, strong attention to detail, and good reading and comprehension skills. Familiarity with annotation tools and the ability to follow specific guidelines are also important, while prior experience or knowledge in the relevant domain can be beneficial but is not always required.
What are popular job titles related to Text Annotation jobs in Ontario? For Text Annotation jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Text Annotation jobs in Ontario look for? The top searched job categories for Text Annotation jobs in Ontario are:
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Re-posted 13 days ago
Thomson Reuters rating
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Job description
Lead Applied Scientist, Document Understanding
About the Role
This role sits within the applied science function. You will own the design, development, and production deployment of document understanding systems that directly power Westlaw, PracticalLaw, and CoCounsel. The problems are real, the scale is large, and the expectation is shipped, reliable, measurable impact.
You will work across semantic chunking, document enrichment, knowledge graph construction, and synthetic data generation for complex legal, tax, and accounting content. Multiple product teams depend on what this function delivers.
About You
You hold a PhD in Computer Science, AI, NLP, or a related field, with 8+ years of post-degree industry experience taking NLP and document understanding systems from development to production at scale. You have hands-on depth across the full applied arc - model development, distillation, evaluation, and deployment. You publish, you mentor, and you measure success by what ships and performs in production.
What You'll Do
Design and deploy semantic chunking models for lengthy, non-uniformly structured legal documents with adjustable granularity across use cases
Build document enrichment systems using legal and customer-defined taxonomies
Develop LLM-based knowledge graph construction pipelines that extract and link citations, entities, and legal concepts across diverse legal content
Lead knowledge distillation efforts to compress large models into latency-constrained, production-ready SLMs
Design evaluation frameworks - component-level and end-to-end - using expert annotation and synthetic data
Own technical decisions on architecture, chunking strategy, classification approach, and knowledge extraction methods
Partner with engineering on delivery, reliability, and scale across multiple product lines
Provide technical input to senior leadership on AI strategy and roadmap
Mentor applied scientists and ML practitioners on the team
Required Qualifications
PhD in Computer Science, AI, NLP, or a related field - required
8+ years of post-degree industry experience shipping document understanding, information extraction, or knowledge graph systems into production - not research-only experience
Publications at ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD, or equivalent
Production Python and experience with PyTorch, Hugging Face Transformers, and DeepSpeed
Hands-on production depth required in:
Document layout analysis and semantic chunking beyond fixed-size or paragraph-based methods
Hierarchical, multi-label document classification with domain-specific and customer-defined schemas
Entity recognition and linking, relation extraction, citation parsing, and knowledge graph construction from unstructured text
LLM-based information extraction, few-shot and multi-task learning, and post-training
Knowledge distillation, model compression, and SLM deployment under latency constraints
Synthetic data generation and annotation workflow design
End-to-end evaluation framework design for document understanding
Preferred Qualifications
Legal document understanding, legal IE, or legal AI experience
Complex document structures: nested hierarchies, cross-references, non-uniform formatting
Retrieval or QA systems over large document collections
RAG and agentic workflows in enterprise settings
Knowledge graph frameworks for legal or enterprise applications
AzureML or AWS SageMaker
#LI-LP2
New Position: This position is open due to an existing vacancy to support our evolving business needs.What's in it For You?
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.
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.
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