Machine Learning Engineer Intern (MPhil/PhD)

Company Description

ReactWise is a seed-stage start-up located in Cambridge, UK that offers powerful no-code software for optimizing chemical reactions. Using machine learning (ML) algorithms developed by the founders during their PhDs at the University of Cambridge, their software can significantly reduce process development time, bringing novel drugs to clinical trials much faster. Several chemical and pharmaceutical companies currently use our software - from start-ups to big pharma. As an early-stage start-up, we offer the opportunity to build software at the cutting edge of machine learning and chemistry, with short iteration cycles and autonomy to shape the software development process.

Role Description

We are looking for Machine Learning Engineer summer intern to join our small, fast-moving team in Cambridge for a 10-week internship. You will work closely with our founders and software engineers to design, develop, and deploy novel ML algorithms for our software platform. This includes ML research, feature engineering, and writing clean, scalable code.

Key responsibilities

• Research and development of novel ML algorithms applied to chemistry.
• Design, implement, and maintain scalable ML applications for our platform.
• Collaborate with the founding team to design ML algorithms to solve client problems.
• Write high-quality, maintainable code with robust unit testing & integration testing.
  

Required skills

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Research experience working with ML libraries in Python (e.g. PyTorch, TensorFlow).
• Strong experience with ML-adjacent technologies.
• Strong problem-solving skills and attention to detail.
• Excellent verbal and written communication skills.
  

Desirable skills

• Currently pursuing an MPhil/PhD in Computer Science, Engineering, or a related field; or equivalent practical experience.
• Research experience and publications within the field of ML for chemistry 
• Basic understanding of chemistry (desirable, but not necessary!)
• Experience with integrating LLMs into application workflows