We are looking for a Machine Learning Research Intern to join our research team in London. We prefer 6 month internships, but we are open to other options.
Summary:
- You will learn to apply machine learning to real-world applications
- You will work with state-of-the-art models on open problems in the field of inference and generation of graph-structured data
- Your work will focus on a 'moonshot project' that can lead to either publication or a paradigm shift in our product
- You will learn fundamental software engineering skills allowing you to develop your work faster and in a more scalable manner
- You will receive a £45,000 pro-rated annual compensation.
Want to learn more, then read on...
nPlan are utilising the world's largest dataset of its kind, along with advanced machine learning, to forecast risk on major infrastructure projects. As our ML Research Intern, you would be working with nERD (nPlan’s Experimental Research Department) to help advance state of the art in the cross section of graph neural networks and large language models with applications in project risk forecasting.
We're working with some of the largest infrastructure projects in the world (think HS2, Heathrow, Crossrail), which means your work could have a real impact on improving the world around us. You’ll be joining a team funded by world-class investors including GV (formerly Google Ventures) tackling some of the most difficult and
We would like you to be:
- Studying towards a research degree (ideally PhD) in machine learning or a related subject.
- Have experience in Python and associated data science libraries (numpy, scipy).
- Have experience in building machine learning models with pytorch, tensorflow or jax.
- Please mention the word 'crane' in your application
- [Optional] have a publication record in top-tier machine learning journals and conferences.
What your typical work week will be like:
As a Machine Learning Research Intern, you will work with other members of nERD, researchers, data scientists and engineers. A typical week could involve a mix of focus time, learning new relevant machine learning and software engineering tools, training and evaluating new machine learning models, code-pairing with other members of nERD, and/or partnering with engineers to work out how to bring a research solution to production. In research, we use recurring meetings to sync our work, but we do not do daily standups. We use Slack, Github, Notion and Google Meet as collaboration tools.
During the internship you will receive continuous support from your line manager and the rest of the research team. A successful internship will ideally conclude with a solution that is i) immediately useful for nPlan, and ii) publishable at a top tier conference. Upon successful completion of the internship we will consider you for a full time role at nPlan.