At a Glance
- Tasks: Develop cutting-edge AI methods for molecular and protein discovery.
- Company: Join a pioneering team at the forefront of scientific AI research.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with exciting projects and career advancement potential.
- Why this job: Make a real impact in science with innovative machine learning techniques.
- Qualifications: PhD or equivalent experience in ML, strong publication record, and programming skills.
The predicted salary is between 60000 - 80000 £ per year.
Machine Learning Scientist (Molecular AI)
About the Role
We are seeking an exceptional Machine Learning Scientist to develop next-generation
AI methods for molecular and protein discovery. You will conduct original research while working closely with engineers to translate new ideas into practical tools that accelerate scientific discovery.
Our ML team values strong scientific contributions. We are particularly interested in candidates with a track record of publishing at top-tier machine learning conferences such as Neur IPS, ICML, ICLR, AISTATS, CVPR, or equivalent venues, or who have demonstrated research of comparable quality.
Responsibilities
- Develop novel machine learning methods for molecules, proteins, and biological systems.
- Design and evaluate state-of-the-art models using modern deep learning techniques.
- Conduct research on graph neural networks, transformers, diffusion models, and geometric deep learning.
- Work with large-scale molecular, protein, and 3D structural datasets.
- Collaborate with engineers to ensure research can be efficiently implemented and deployed.
- Communicate work internally and externally.
- Stay at the forefront of advances in AI for scientific discovery.
Qualifications Required
- Ph D (or equivalent research experience) in Machine Learning, Computer Science, Computational Biology, Physics, Mathematics, or a related discipline.
- Strong publication record at premier ML conferences (e. g., Neur IPS, ICML, ICLR, AISTATS, CVPR) or equivalent research achievements.
• Deep understanding of modern deep learning methods, including
- Graph Neural Networks (GNNs)
- Transformer architectures
- Diffusion models
- Strong programming skills in Python and Py Torch.
- Ability to independently drive research from idea to implementation.
- Excellent communication and collaboration skills.
- Preferred
- Experience with 3D machine learning, geometric deep learning, or spatial representations.
- Experience working with molecular structures, proteins, drug discovery, or computational chemistry.
- Familiarity with molecular generation, protein modeling, docking, or structure prediction.
- Experience scaling experiments and collaborating with engineering teams to translate research into production systems.
