Machine Learning Research Engineer; Foundational Research

Join a cutting‑edge research team working to deliver on the transformation promises of modern AI. We are seeking Machine Learning Research Engineers with the skills and drive to build and conduct experiments with advanced AI systems in an academic environment rich with high‑quality data from real‑world problems.

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 expanding our strong foundation of research capabilities across different areas and are looking for engineers who participate in designing coding conducting experiments and translating findings into concrete deliverables.

• LLM Training (Continued pretraining instruction tuning reinforcement learning distributed training efficient ML techniques)
• Post‑training techniques for planning reasoning & complex workflows (e.g. reasoning models LLMs knowledge graphs test time compute CoT pipelines tool use & API calling etc.)
• Data‑centric Machine Learning (Synthetic data curriculum learning learned data mixtures etc.)
• Evaluation (Benchmarking best practices humans / LLMs as a judge red teaming / adversarial testing hallucination detection etc.)

We work collaboratively with TR Labs (TRs applied research division) academic partners at world‑leading research institutions 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 an ML Research Engineer you will play a key part in 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 contribute to our proprietary AI model research & development through rapid prototyping scalable infrastructure and production‑quality implementations and to research papers in top tier academic conferences and journals.

• Build :
  You will design and implement robust scalable systems for training and evaluating large language models. You’ll build data pipelines for data‑centric research training infrastructure for instruction fine‑tuning (IFT) Direct Preference Optimization (DPO) and reinforcement learning workflows evaluation frameworks for comprehensive model assessment and infrastructure for agentic workflows that enables researchers to iterate quickly and effectively.
• Innovate :
  You will work at the very cutting edge of AI Research at an institution with some of the richest data sources in the world. Through your work you will rapidly implement novel research ideas in LLM training evaluation agentic systems and data processing transforming them into production‑ready systems and research publications. You will contribute to advancing the state‑of‑the‑art in data‑centric ML by building tools that help us make the best use of our unprecedented data resources.
• Experiment and Develop :
  You are involved in the entire research & model development lifecycle brainstorming coding testing and delivering high‑quality implementations that support cutting‑edge research. You’ll build pipelines for synthetic data generation automated evaluation systems training workflows for various fine‑tuning approaches and agent‑based workflows that push the boundaries of what’s possible with LLMs.
• Collaborate :
  Working on a collaborative global team of research engineers and scientists both within Thomson Reuters and our academic partners at world‑leading universities. You’ll work closely with researchers to understand their needs and translate cutting‑edge research papers into practical scalable implementations.
• Communicate :
  Actively engage…