We are looking for a Research Scientist to develop new methods in modelling, reasoning, and experiment automation that power our AI-driven material discovery engine. You will own the research direction for problems in this space: designing experiments, evaluating new approaches, and working with engineers to scale what works onto real materials science problems.
You will be joining a small, highly ambitious team of world-renowned engineers, AI researchers, and materials scientists. We move fast and value people who are energised by that.
This is a role for someone who has a deep understanding of ML research, is excited about pushing the boundaries of what models can do in scientific domains, and wants to make a meaningful contribution to material science.
What You'll DoFormulate and prototype novel machine learning architectures (e.g., foundation models, GNNs, generative models) tailored specifically to the complexities of materials science and chemistry.
Design active learning and optimization algorithms (such as Bayesian optimization or reinforcement learning) that act as the "brain" of our platform, deciding which physical experiments our autonomous lab should run next.
Tackle fundamental research challenges in representation learning, specifically devising ways to train effectively on the sparse, noisy, and highly dimensional data generated by real-world physical experiments.
Collaborate closely with materials scientists and chemists to translate physics, constraints, and scientific intuition into rigorous mathematical models and novel loss functions.
Drive the research lifecycle from theoretical ideation to proof-of-concept, establishing strong baselines and proving out the viability of new algorithmic approaches before partnering with engineering to scale them.
Stay at the absolute bleeding edge of machine learning literature, identifying breakthrough techniques from adjacent fields and rapidly adapting them to accelerate our discovery engine.
PhD in computer science, machine learning, physics, or a closely related field; experience with scientific or simulation domains strongly preferred.
Proven track record of developing and evaluating novel ML methods, with a clear understanding of training dynamics and generalisation behaviour.
Strong Python skills and production-quality research code; experience with PyTorch or an equivalent ML framework.
Evidence of significant research impact through publications, open-source work, or applied research projects.
Comfortable working in Linux-based environments, with version control (Git) and HPC or cloud platforms.
Experience applying ML in a scientific, simulation, or research computing setting.
Experience with large-scale or distributed training on GPU clusters.
Familiarity with scientific data formats and reproducibility practices.
Contributions to open-source ML or scientific computing packages.
Diffractive is building the AI Material Scientist that autonomously learns from real-world experimentation to push the boundaries of scientific discovery. We're early, moving fast, and working on problems that genuinely matter.
You'll join a small, high-calibre team where your work has real impact from day one. We're London-based with a flexible approach to how and where you work. We offer competitive salary, generous equity and benefits. You'll have a real stake in what you build and in the company's overall success.
How to Apply
If you're excited about this role and believe you could thrive in it, we'd encourage you to apply even if you may not align with every part of the job description.
Diffractive is an equal opportunities employer. We are committed to creating an inclusive environment for all employees and welcome applications from people of all backgrounds, experiences, and identities.
If you require any adjustments or accommodations at any point during the interview process please let us know - we will be happy to help.
Hit the apply button below to submit your application. We are looking forward to hearing from you!
