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Machine Learning Research Scientist

2 months ago

London, Greater London, United Kingdom Isomorphic Labs Limited Full time
Research Scientist in Machine Learning

Isomorphic Labs Limited is a cutting-edge company that is revolutionizing drug discovery through a computational- and AI-first approach.

We are seeking a highly skilled Research Scientist in Machine Learning to join our team. As a key member of our research team, you will play a critical role in building and developing cutting-edge machine learning models and algorithms that will transform the drug discovery world.

Working in a fast-paced and interdisciplinary environment, you will collaborate with leading engineers and scientists to design and develop innovative machine learning solutions that will unlock new modelling and predictive power.

You will draw upon your existing research experience to apply novel techniques and ideas to complex computational biology and chemistry problems.

Depending on your experience, you will have the opportunity to lead projects, bring together a team of scientists and engineers to pursue ambitious modelling problems, and provide technical mentorship and guidance to others in the ML community.

Key Responsibilities:

  • Develop and implement novel machine learning techniques and algorithms
  • Collaborate with cross-functional teams to design and develop innovative solutions
  • Contribute to research directions in machine learning and apply world-leading ML algorithms to drug discovery
  • Identify and create novel ML techniques and required data to train
  • Develop and implement machine learning models and algorithms
  • Report and present research findings and developments clearly and efficiently

Requirements:

  • PhD or equivalent practical experience in a technical field
  • Proven track record in machine learning using deep learning techniques
  • Experience using ML frameworks such as JAX, PyTorch, or TensorFlow
  • Passion for applying ML research to real-world problems

Experience in any of the following areas is highly desirable:

  • Large-scale deep learning
  • Generative models
  • Graph neural networks
  • Deep learning for drug discovery
  • Deep learning for computer vision
  • 3D graphics/robotics
  • Real-world applied RL