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.

• →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

• 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

• 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

• Experience at the intersection of cryptography and machine learning

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

• Don’t ask for permission - we have a constraint culture, not a permission culture.
• Claim ownership of any work stream and set its goals/deadlines, rather than waiting to be assigned work or relying on job specs.
• Push & pull context on your work rather than waiting for information from others and assuming people know what you’re doing.
• Communicate to be understood rather than pushing out information and expecting others to work to understand it.
• Stay a small team - misalignment and politics scale super-linearly with team size. Small protocol teams rival much larger traditional teams.

• Give direct feedback to everyone immediately - rather than avoiding unpopularity, expecting things to improve naturally, or trading short-term pain for extreme long-term pain.
• Embrace an extreme learning rate - rather than assuming limits to your ability / knowledge.
• Don’t quit - push to the final outcome, despite any barriers.
• Be anti-fragile - balance short-term risk for long-term outcomes.
• Reject waste - guard the company’s time, rather than wasting it in meetings without clear purpose/focus, or bikeshedding.