ML Research Scientist – Probabilistic Inference

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

We are seeking a Machine Learning (ML) Research Scientist specialising in probabilistic inference to join a highly technical research team working at the forefront of machine learning and probabilistic modelling.

In this role, you will develop and evaluate advanced probabilistic inference methods, with a particular focus on amortised inference, translating theoretical insights into practical, scalable implementations.
You will work closely with mathematicians, ML researchers and engineers to develop new approaches to learning and inference in complex probabilistic models, design rigorous evaluation methodologies and use experimental results to drive future research directions.
This is an opportunity to work on challenging problems spanning probabilistic inference, deep learning, probabilistic graphical models and advanced machine learning.

Key Responsibilities

• Develop novel amortised inference methods suitable for high-dimensional discrete and continuous probability distributions.
• Develop parameter-learning and structure-learning methods for large-scale probabilistic graphical models that benefit from amortised probabilistic inference.
• Design rigorous evaluation strategies for machine learning methods that rely on probabilistic inference.
• Collaborate closely with mathematicians and researchers on theoretical questions relating to learning and inference in probabilistic models.
• Translate theoretical concepts and research proposals into high-quality, maintainable implementations using languages such as Python.
• Design and execute experiments to evaluate new inference and learning approaches.
• Analyse and interpret experimental results to identify insights and inform future research directions.
• Communicate complex technical findings clearly to researchers, engineers and other stakeholders.
• Contribute to research planning, technical discussions and the development of new methodologies.

Background & Experience

• Advanced degree in a relevant field, such as Computer Science, Mathematics, Machine Learning, Statistics or a related discipline.
• PhD preferred, but not required for candidates demonstrating exceptional technical and research capabilities.
• Minimum of 3 years of experience in deep learning or machine learning research.
• Strong expertise in probabilistic inference.
• Strong mathematical background, with an understanding of probability, statistics and related mathematical concepts.
• Proven experience developing and implementing machine learning models.
• Track record of contributing to high-quality research in probabilistic inference, probabilistic modelling or a closely related field.

Areas of Expertise
In addition to probabilistic inference, experience in one or more of the following areas is highly relevant:

• Bayesian inference
• Sampling-based approximate inference
• Amortised inference, including variational inference and Generative Flow Networks
• Parameter and/or structure learning in probabilistic graphical models
• Causal modelling
• Reinforcement learning
• Optimal control

Technical Skills

• Strong programming skills in Python or a comparable language.
• Experience with machine learning frameworks such as PyTorch or TensorFlow.
• Experience implementing research algorithms and translating mathematical concepts into working software.
• Strong analytical and problem-solving abilities, with the capacity to reason about complex mathematical and computational systems.
• Experience designing and running rigorous computational experiments.

Collaboration & Communication

• Excellent written and verbal communication skills.
• Ability to explain complex mathematical and technical concepts to audiences with different areas of expertise.
• Strong collaborative approach and experience working within multidisciplinary research teams.
• Self-motivated and capable of independently driving research projects.
• Ability to work effectively in an environment where research questions and technical approaches evolve over time.

What You’ll Bring
We are looking for a mathematically strong ML researcher with deep expertise in probabilistic inference and a genuine interest in translating theoretical ideas into practical machine learning systems.
You will be comfortable working at the intersection of mathematical theory, probabilistic modelling and machine learning implementation, moving from theoretical hypotheses through to experimentation, evaluation and working software.