Senior ML Research Scientist

Our client is a non-profit organization committed to advancing research and creating technical solutions that enable safe-by-design AI systems.

They are seeking a Senior Machine Learning (ML) Research Scientist to join a highly technical research team working at the forefront of machine learning and advanced AI systems.

In this role, you will design, develop and evaluate innovative machine learning models and approaches for complex research problems. You will work closely with researchers, mathematicians and ML engineers to translate theoretical advances into practical algorithms, develop experimental methodologies and evaluate models at frontier scale.
This is an opportunity to contribute to challenging research projects spanning deep learning, large-scale model development and advanced AI research, with significant scope to shape research direction and technical approaches.

Key Responsibilities

• Propose, design and implement novel machine learning models and methodologies to address complex research problems.
• Collaborate with mathematicians and specialised research scientists to translate theoretical developments into practical ML algorithms and models.
• Adapt and fine-tune existing frontier models for specific applications, domains and research scenarios.
• Design and implement rigorous experimental protocols and evaluation frameworks to test hypotheses and produce robust, reproducible results.
• Develop experiments capable of operating at the scale and complexity required for frontier models.
• Analyse and interpret experimental results to identify new research directions and inform future hypotheses.
• Benchmark and optimise model performance, efficiency and resource utilisation in collaboration with ML engineers.
• Improve training and inference efficiency across long-running and computationally intensive experiments.
• Communicate research findings clearly and collaborate closely with other researchers and engineers.
• Contribute to research planning, technical discussions and the development of new research methodologies.

Background & Experience

• Advanced degree in a relevant field, such as Computer Science, Mathematics, Machine Learning or a related discipline.
• PhD preferred, but not required for candidates demonstrating exceptional technical and research capabilities.
• 5+ years of experience working on deep learning research projects, particularly involving large-scale or frontier models.
• Demonstrated experience training, adapting and/or fine-tuning large-scale machine learning models.
• Experience working with complex model adaptation techniques such as transfer learning, domain adaptation or meta-learning.
• Proven track record of contributing to high-quality machine learning or deep learning research.

Technical Skills

• Strong expertise with ML frameworks such as PyTorch, TensorFlow or JAX.
• Experience developing, training and evaluating machine learning models in distributed computing environments.
• Strong Python development skills, with experience writing research-quality and production-grade code.
• Strong understanding of software development practices, including version control, collaborative development and experiment management.
• Experience using experiment tracking and management tools to ensure research is reproducible and well documented.
• Ability to benchmark and optimise model performance across large-scale computational workloads.

Collaboration & Communication

• Excellent written and verbal communication skills.
• Ability to explain complex technical concepts and exchange ideas effectively with researchers across different disciplines.
• Strong collaborative approach, with the ability to work effectively within multidisciplinary research teams.
• Self-motivated and capable of independently driving research projects from initial hypothesis through to implementation and evaluation.
• Comfortable working in an environment where research priorities and technical approaches evolve rapidly.

Preferred Skills & Experience

• Experience analysing the behaviour and performance of frontier models in complex or safety-critical applications.
• Experience designing or developing model evaluation frameworks, alignment methodologies or advanced ML benchmarks.
• Experience in Natural Language Processing (NLP).
• Experience with probabilistic graphical models or related statistical modelling techniques.
• Publications or other demonstrable contributions to high-quality deep learning research.
• Experience working with large-scale distributed training and inference infrastructure.