The Role
- 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
