Leap Laboratories is a VC funded deep tech startup working on the cutting-edge of machine learning interpretability and knowledge discovery. We are seeking a highly motivated and experienced machine learning researcher to join our growing team.

More about us here: https://leap-labs.com/

What to expect

You’ll work within a small team of 8 people to conduct high-impact research in AI interpretability, AutoML and AI for scientific discovery, focusing on both fundamental theory and practical applications.

You’ll contribute to existing research projects and lead new ones, developing novel techniques to train and interpret deep neural networks for knowledge discovery. You’ll stay abreast of current work in AI and interpretability research, write clean, efficient code, publish academic papers, and present your work at international venues.

We’re based in London and San Francisco, and expect this role to be based in London at our Old Street office. We operate a hybrid work policy, and typically work in person 3+ days a week.

The Ideal Candidate

Essentials:

• PhD, or equivalent practical experience in a technical field, demonstrating ability to produce novel research.
• Proven creative problem solving under uncertainty. We often work in uncharted territory, and have to figure out tough problems without much guidance from existing literature (because it doesn’t exist).
• Complementarily, a broad understanding of the state of the art in machine learning, and ability to efficiently identify useful existing work as well as promising research avenues.
• Hands-on deep learning experience, including benchmarking existing techniques, prototyping research ideas, designing new architectures or algorithms.
• Ability to write efficient, organised code that's easy to read and maintain.
• Effective oral and written communication for internal collaborations, as well as presentation of your research to the outside world.
• Strong documentation habits, keeping detailed logs of ideas, experiments, and results.
• Excellent time-management and organisation skills.

Nice to have

• Experience in AI interpretability, explainability, or AI safety; AutoML; and/or AI in science.
• Track record of machine learning publications.
• Experience working with real-world scientific datasets.