Machine Learning Research Engineer (Foundational Research)

Machine Learning Research Engineer (Foundational Research)

Join a cutting-edge research team at Thomson Reuters to help deliver the transformation promises of modern AI. You will design, build, and experiment with large language models (LLMs) in an academic environment backed by high-quality real-world data.

About the Role

As an ML Research Engineer, you will:

• Build: Design and implement scalable training and evaluation systems for LLMs, including data pipelines, instruction fine-tuning (IFT), Direct Preference Optimization (DPO), reinforcement learning workflows, and agentic workflow infrastructure.
• Innovate: Rapidly prototype novel research ideas in LLM training, evaluation, agentic systems, and data processing, turning them into production-ready systems and research publications.
• Experiment and Develop: Drive the full research and model development lifecycle, from brainstorming and coding to testing and delivering high-quality implementations that support cutting-edge research.
• Collaborate: Work closely with a global team of research engineers and scientists, both within Thomson Reuters and with academic partners at world-leading universities.
• Communicate: Share technical implementations and best practices through code reviews, documentation, technical presentations, and knowledge-sharing sessions.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a relevant discipline (or equivalent practical experience).
• 3+ years of hands-on experience building ML/NLP/AI systems with strong software engineering practices.
• Demonstrated expertise in building production-quality code and data pipelines for ML systems.
• Proficiency in modern AI development frameworks, including PyTorch, JAX, HuggingFace Transformers, vLLM, and LLM APIs.
• Understanding of LLM training methodologies, including instruction fine-tuning, preference optimization, and reinforcement learning approaches.
• Strong software engineering skills: version control, testing, CI/CD, and code-quality practices.
• Hands-on experience with experiment tracking tools such as ClearML, Weights & Biases, and MLflow.
• Experience with distributed computing frameworks and large-scale data processing (e.g., Ray, Spark, Dask).
• Excellent communication skills to collaborate with researchers and translate research ideas into robust implementations.
• Self-driven attitude with a genuine curiosity about ML research developments.
• Comfortable working in fast-paced, agile environments, managing uncertainty and ambiguity of novel research.

Helpful Qualifications

• Track record of ML impact in releases, publications, or open-source contributions, especially in training, evaluation, data processing, or agent systems.
• Experience building and maintaining ML training infrastructure and data pipelines at scale.
• Experience with instruction fine-tuning (IFT), Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), or other RLHF methods.
• Hands-on experience implementing and scaling supervised fine-tuning, preference learning, and reinforcement learning pipelines for LLMs.
• Experience building LLM evaluation frameworks, benchmarking systems, or automated testing pipelines.
• Hands-on experience with agentic workflows, tool-using AI systems, or multi-agent coordination (e.g., LangGraph, AutoGPT, LLamaIndex).
• Experience with data-centric ML approaches, including synthetic data generation, curriculum learning, or data curation pipelines.
• Experience training large-scale models over distributed nodes using cloud tools such as AWS, Azure, or Google Cloud.
• Hands-on experience with MLOps, experiment tracking, and model deployment systems.
• Strong interest in keeping up with ML research literature and quickly implementing novel techniques from academic papers.
• Familiarity with training optimization techniques such as mixed precision, gradient checkpointing, and efficient attention mechanisms.
• Knowledge of modern ML engineering practices (containerization, orchestration, monitoring).

Benefits and Compensation

• Competitive compensation & benefits package.
• Hybrid work model: office 2-3 days a week with flexible remote work up to 8 weeks per year.
• Flexibility & work-life balance policies, including paid vacation, mental health days, and Headspace app access.
• Career development programs, tuition reimbursement, and growth opportunities in an AI-enabled future.
• Inclusive culture, belonging, and social impact initiatives (volunteer days, ESG projects).

Location

London, United Kingdom

Equal Opportunity Employer

Thomson Reuters is proud to be an Equal Employment Opportunity Employer. We provide a drug-free workplace and make reasonable accommodations for qualified individuals with disabilities and sincerely held religious beliefs in accordance with applicable law.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a relevant discipline (or equivalent practical experience).
• 3+ years of hands-on experience building ML/NLP/AI systems with strong software engineering practices.
• Demonstrated expertise in building production-quality code and data pipelines for ML systems.
• Proficiency in modern AI development frameworks, including PyTorch, JAX, HuggingFace Transformers, vLLM, and LLM APIs.
• Understanding of LLM training methodologies, including instruction fine-tuning, preference optimization, and reinforcement learning approaches.
• Strong software engineering skills: version control, testing, CI/CD, and code-quality practices.
• Hands-on experience with experiment tracking tools such as ClearML, Weights & Biases, and MLflow.
• Experience with distributed computing frameworks and large-scale data processing (e.g., Ray, Spark, Dask).
• Excellent communication skills to collaborate with researchers and translate research ideas into robust implementations.
• Self-driven attitude with a genuine curiosity about ML research developments.
• Comfortable working in fast-paced, agile environments, managing uncertainty and ambiguity of novel research., * Track record of ML impact in releases, publications, or open-source contributions, especially in training, evaluation, data processing, or agent systems.
• Experience building and maintaining ML training infrastructure and data pipelines at scale.
• Experience with instruction fine-tuning (IFT), Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), or other RLHF methods.
• Hands-on experience implementing and scaling supervised fine-tuning, preference learning, and reinforcement learning pipelines for LLMs.
• Experience building LLM evaluation frameworks, benchmarking systems, or automated testing pipelines.
• Hands-on experience with agentic workflows, tool-using AI systems, or multi-agent coordination (e.g., LangGraph, AutoGPT, LLamaIndex).
• Experience with data-centric ML approaches, including synthetic data generation, curriculum learning, or data curation pipelines.
• Experience training large-scale models over distributed nodes using cloud tools such as AWS, Azure, or Google Cloud.
• Hands-on experience with MLOps, experiment tracking, and model deployment systems.
• Strong interest in keeping up with ML research literature and quickly implementing novel techniques from academic papers.
• Familiarity with training optimization techniques such as mixed precision, gradient checkpointing, and efficient attention mechanisms.
• Knowledge of modern ML engineering practices (containerization, orchestration, monitoring).

• Competitive compensation & benefits package.
• Hybrid work model: office 2-3 days a week with flexible remote work up to 8 weeks per year.
• Flexibility & work-life balance policies, including paid vacation, mental health days, and Headspace app access.
• Career development programs, tuition reimbursement, and growth opportunities in an AI-enabled future.
• Inclusive culture, belonging, and social impact initiatives (volunteer days, ESG projects).

Join a cutting-edge research team at Thomson Reuters to help deliver the transformation promises of modern AI. You will design, build, and experiment with large language models (LLMs) in an academic environment backed by high-quality real-world data.