Research Intern

Contribute to cutting-edge research in scalable, distributed machine learning systems alongside experienced researchers and engineers. Explore new ways of building and verifying neural networks that operate across huge, decentralised, topologies of heterogenous devices.

Responsibilities

Contribute to original research in deep learning with a focus on modular architectures, verifiability, continual learning, and scale

Design and prototype novel neural network architectures for decentralized compute environments

Contribute to joint publications and projects in collaboration with academic and industry researchers targeting top-tier AI venues such as NeurIPS, ICML, and ICLR

Competencies

Must Have

Currently enrolled in a PhD program (or, in exceptional cases, in a Master’s program) in Computer Science, Machine Learning, or a related field

Prior experience conducting original research, ideally with authorship or co-authorship on ML papers

Strong understanding of deep learning fundamentals and experience working with in at least one major framework, e.g. PyTorch, JAX, or TensorFlow

Self-directed, curious, and able to thrive in an environment with high autonomy

Excellent written and verbal communication skills

Preferred

Research experience in distributed systems, continual learning, or modular neural architectures

A desire to contribute to open research and collaborate with the broader ML research community

Nice to Have

Experience at the intersection of cryptography and machine learning

Please note: the benefits listed below apply to full-time employees only