Applied Research Internships At Thomson Reuters | WFH(Hybrid)

Qualifications

This is our wish list. We understand that few people check every box. If you are excited about this opportunity, meet most of the following criteria, and are eager to learn, we want to hear from you!

• Have an understanding and experience with Natural Language Processing, and/or Machine Learning methods including Deep Neural Networks
• Are experienced in one or more programming languages (preferably Python) and have experience working in the Cloud (preferably AWS)
• Have experience with ML/NLP libraries and frameworks such as PyTorch, TensorFlow, scikit-learn, spaCy, Hugging Face Transformers, LangChain, LangGraph, LlamaIndex, Haystack, FlashRAG, and modern generative AI APIs (e.g. Gemini, OpenAI, or Anthropic) for building RAG and GraphRAG applications.
• Are comfortable working with unstructured datasets and are knowledgeable about tools and techniques for data cleaning and processing
• Have familiarity with common NLP use cases such as chatbot development, information retrieval, language translation, named entity recognition, summarization and topic modeling
• Are a student (or hold a degree) in Computer Science, Engineering, Statistics, Mathematics, or other quantitative disciplines
• Have good communication skills and the ability to work collaboratively as part of a team

Roles & Responsibilities:

The internship duration at Thomson Reuters Labs will be six months and may be aligned with an academic semester or depending upon your availability, can begin immediately. The role would be based out of Bangalore. We currently follow a hybrid office model wherein selected candidate(s) would need to be present physically in office for at least 2 days a week during the duration of the internship.

As a research scientist intern you will:  

• Experiment and Develop: You can be involved in the entire model development lifecycle, building, testing, and delivering high-quality solutions.
• Collaborate: Working on a collaborative cross-functional team, you will learn from and partner with colleagues across the globe.
• Innovate: You will have the opportunity to try new approaches and learn new technologies. You will contribute ideas and work on solving real-world challenges.