Research Scientist Intern, Thomson Reuters

• PhD student or recent graduate with research experience in a relevant discipline
• Publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, ICLR)
• Familiarity with a deep learning framework (e.g. pytorch, jax, tensorflow, …)
• Excellent communication skills to report and present research findings and developments clearly, both orally and in writing
• Curious and innovative disposition capable of devising novel, well-founded algorithmic solutions to relevant problems

Desirable

• We are seeking Research Scientist Interns with flexible starting dates throughout the year in our London, Toronto & Zug locations
• During your internship, you will focus on publishing high-quality research in top venues for Machine Learning & NLP while advancing our internal model development
• We also value our deep academic connections, are open to involving academic advisors & collaborators
• Foundational Research is the dedicated core Machine Learning research division of Thomson Reuters
• We are focused on research and development, with a particular focus on advanced algorithms and training techniques for Large Language Models (LLMs)
• We are building a strong foundation of research capabilities across different areas and are looking for interns who participate in designing, coding, conducting experiments, translating findings into concrete deliverables and engaging with the academic community. Our focus areas are:
• LLM Training (Continued Pretraining, Instruction Tuning, Reinforcement Learning Algorithms & Infrastructure, Alignment, Distributed Training, …)
• Post-training techniques for planning & reasoning (e.g. Agentic pipelines & tool use, LLMs & Knowledge Graphs, Self-reflection & critique, CoT & Reasoning, RAG, …)
• Data-centric Machine Learning (Synthetic & Hybrid Data generation, Curriculum Learning, learned data-mixtures, …)
• Evaluation (Benchmark design, Red-teaming/Adversarial Testing, Hallucination detection & Factuality, Human-in-the-loop testing, ...)
• We work collaboratively with academic partners at world-leading research institutions (such as our joint academic lab with Imperial College London) and subject matter experts with decades of experience
• We experiment, prototype, test, and deliver ideas in the pursuit of smarter and more valuable models trained on an unprecedented wealth of data and powered by state-of-the-art technical infrastructure
• Through our unique institutional experience, we have access to an unprecedented number of subject matter experts involved in data collection, testing, and evaluation of trained models
• As a Research Scientist Intern, you will work alongside and learn from a diverse global team of experts
• We hire world-leading specialists in ML/NLP/GenAI, as well as Engineering, to drive the company’s leading internal AI model development
• You will have the opportunity to publish your research findings as well as contribute to our proprietary AI model research & development
• Thomson Reuters is known for consistently delivering successful data-driven ML solutions in pursuit of academic excellence and support of high-growth products that serve Thomson Reuters customers in new and exciting ways
• The internship duration at Thomson Reuters Labs is typically 4 to 6 months and may be aligned with one or two academic semesters or depending upon your availability
• In this opportunity, as a Research Scientist Intern you will:
• Innovate: You will have the opportunity to innovate and create new state-of-the-art ML/NLP/IR/GenAI approaches at the cutting edge of AI research. You will work closely with a Research Scientist to contribute ideas and work on solving real-world challenges using a wealth of data
• Experiment and Develop: You are involved in the entire research & model development lifecycle, brainstorming, coding, testing, and delivering high-quality reports at leading international academic conferences
• Collaborate: Working on a collaborative global team of researchers & engineers both within Thomson Reuters and our academic partners at world-leading universities
• Communicate: Actively engage in sharing our technical findings with the wider community through contributions to seminars, lectures, conferences and/or the sharing of publications and/or technical assets (data & models)