Machine Learning Research Scientist / Research Engineer
CV-LibraryLondonpermanentPosted: 3 March 2026
Apply NowWe are partnering exclusively with a pioneering organisation developing next‑generation computing technologies to appoint a Machine Learning Research Scientist/Research Engineer. This is a rare opportunity to join a team working at the intersection of machine learning, computational science, and emerging hybrid computing architectures. The work focuses on algorithmic innovation, generative modelling, and benchmarking ML systems on cutting‑edge hardware platforms.
You will contribute to the development of new machine learning algorithms designed for advanced computing systems that extend beyond traditional GPU/TPU environments. The role involves close collaboration with researchers across ML, physics, and scientific computing, with a strong emphasis on generative models, numerical methods, and hybrid quantum‑classical approaches.
Essential Experience
Experience with heterogeneous computing hardware (HPC, NPUs, ASICs, quantum systems).
Strong background in machine learning research, including algorithm development and benchmarking.
Hands‑on experience with generative models (diffusion, flows, GANs).
Proficiency in PyTorch, Python, and modern ML tooling.
Experience in computational science, numerical methods, or scientific computing.
MSc or PhD in Machine Learning, Computer Science, Physics, Applied Mathematics, or a related field, or equivalent research experience.
This role is ideal for candidates who thrive in research‑driven environments, enjoy developing new ML methods, and want to work on problems that blend machine learning, physics, and advanced computation. Due to the nature of the work, this role is best suited to candidates with a strong research background rather than purely production‑focused ML experience.
If you’re excited by the idea of building ML algorithms for non‑traditional compute architectures, we’d welcome a confidential conversation
You will contribute to the development of new machine learning algorithms designed for advanced computing systems that extend beyond traditional GPU/TPU environments. The role involves close collaboration with researchers across ML, physics, and scientific computing, with a strong emphasis on generative models, numerical methods, and hybrid quantum‑classical approaches.
Essential Experience
Experience with heterogeneous computing hardware (HPC, NPUs, ASICs, quantum systems).
Strong background in machine learning research, including algorithm development and benchmarking.
Hands‑on experience with generative models (diffusion, flows, GANs).
Proficiency in PyTorch, Python, and modern ML tooling.
Experience in computational science, numerical methods, or scientific computing.
MSc or PhD in Machine Learning, Computer Science, Physics, Applied Mathematics, or a related field, or equivalent research experience.
This role is ideal for candidates who thrive in research‑driven environments, enjoy developing new ML methods, and want to work on problems that blend machine learning, physics, and advanced computation. Due to the nature of the work, this role is best suited to candidates with a strong research background rather than purely production‑focused ML experience.
If you’re excited by the idea of building ML algorithms for non‑traditional compute architectures, we’d welcome a confidential conversation