Research Engineer

The Ellison Institute of Technology (EIT) Oxford’s purpose is to have a global impact by fundamentally reimagining the way science and technology translate into end-to-end solutions and delivering these solutions in programmes and platforms that respond to humanity’s most challenging problems. 

EIT Oxford will ensure scientific discoveries and pioneering science are turned into products for the benefit of society that can have high-impact worldwide and, over time, be commercialised to ensure long-term sustainability. 

Led by a faculty of world experts, EIT Oxford seeks to solve the world’s most challenging problems across four high-risk, high-reward, high-impact humane endeavours: health and medical science; food security and sustainable agriculture; climate change and clean energy; and government innovation in an era of artificial intelligence. 

EIT Oxford is investing significant resources in a new world-class research and development facility in the Oxford Science Park. Set for completion in 2027, the state-of-the-art campus includes 300,000 sq ft of research laboratories, an oncology and preventative care clinic, and educational and meeting spaces. Together, they create the perfect environment for EIT Oxford experts to take ground-breaking ideas from research to broad implementation. The new facility will further EIT’s current partnership with the University of Oxford and become the new home for Ellison Scholars. 

EIT Oxford is committed to cultivating a community where excellence is achieved through collaboration, trust, innovation and tenacity. We foster an environment where everyone’s experience and expertise are valued. We are curious and resilient in our efforts to drive long-term, sustainable innovation to meet humanity’s most enduring challenges. 

 
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. 

Qualifications 

• 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.