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Deep Learning Scientist


We are looking for a machine learning expert who is passionate about building models for real-world applications. Intellegens is a spin-out from the University of Cambridge that has developed a unique deep learning architecture that can work with sparse and noisy data. We are currently working in the material, chemical and drug discovery sectors and as the technique is generic and we have further opportunities in other domains such as finance, retail, and Internet of Things.

About the role

  • Work on complex data sets from some of the world’s largest organisations

  • Use our technology alongside maths, statistics and off the shelf machine learning custom techniques to derive key insights across various industry sectors including pharma, health and high-tech

  • Work on real world, high value, machine learning problems and technology which has the potential to scale to global levels

Essential skills

  • Educated to a MSc or PhD level in the field of Computer Science, Machine Learning, Applied Statistics, or Mathematics

  • Ability to clearly communicate the designed algorithms, data flows and outcomes

  • Highly motivated self-start with strong delivery of results

  • Experience in statistical modelling and machine learning

  • Flexible, adaptable and pro-active with a ‘can-do’ approach

  • Familiar working in Unix environments, and experienced in working in 3 of the following: Fortran, GPU optimization, Java, C++, Python, Scala, GoLang, Docker, AWS, REST API’s, Spark, Hadoop

Good to have

  • Experience in materials science or drug discovery

  • Application of deep learning technology to real-world problems

  • Experience communicating with key stake holders


  • Flexible working environment
 – to be a part of a team with no red tape or bureaucracy

  • An advanced environment in which you can utilize, and learn, the newest and most innovative research in Machine learning, Deep Neural Networks, Reinforcement Learning, etc.

  • You can choose and advise on the best technologies to use

  • Competitive remuneration – including travel and expenses

  • Share options and exceptional career opportunities as a member of the early team

Office address

Barclays Eagle Lab
28 Chesterton Road
CB4 3AZ Cambridge

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