Led by a world-class faculty of scientists, technologists, policy makers, economists and entrepreneurs, the Ellison Institute of Technology aims to develop and deploy commercially sustainable solutions to solve some of humanity's most enduring challenges. Our work is guided by four Humane Endeavours: Health, Medical Science & Generative Biology, Food Security & Sustainable Agriculture, Climate Change & Managing Atmospheric CO2 and Artificial Intelligence & Robotics.
Set for completion in 2027, the EIT Campus in Littlemore will include more than 300,000 sq ft of research laboratories, educational and gathering spaces. Fuelled by growing ambition and the strength of Oxford's science ecosystem, EIT is now expanding its footprint to a 2 million sq ft Campus across the western part of The Oxford Science Park. Designed by Foster + Partners led by Lord Norman Foster, this will become a transformative workplace for up to 7,000 people, with autonomous laboratories, purpose-built laboratories including a plant sciences building and dynamic spaces to spark interdisciplinary collaboration.
Job summary
We are looking for Research Engineers who will work with large scale datasets from different modalities, including biomolecular data, such as proteins and DNA, 2D and 3D images or graph-structured data. You will design and implement advanced ML architectures and workflows encompassing multimodal generative models and physics-based modelling.
Responsibilities
* Design and implement advanced ML architectures
* Develop robust model evaluation pipelines.
* Optimize model architectures for performance and reliability.
* Drive best practices in code quality, reproducibility, and collaboration.
* Build and maintain large-scale ML systems that process biomolecule datasets.
* Work with domain specialists to incorporate wet lab data and HPC simulations.
* Help to build and maintain large-scale generative models of molecular data.
Requirements
* Advanced degree in Computer Science, Physics, Chemistry, or Materials Science with a strong ML focus.
* Hands-on experience in GPU-based computing, HPC integrations, and distributed system architectures.
* Proficiency with deep learning frameworks (e.g. PyTorch) and scientific software (e.g., molecular simulation).
* Strong understanding of data modeling and pipeline optimization for large-scale experiments.
* Ability to collaborate effectively with multidisciplinary teams and communicate complex concepts clearly.
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