About the role
Job Application for Research Intern at Gensyn Back to jobs Research Intern Remote Apply Machine intelligence will soon take over humanity’s role in knowledge-keeping and creation. What started in the mid-1990s as the gradual off-loading of knowledge and decision making to search engines will be rapidly replaced by vast neural networks - with all knowledge compressed into their artificial neurons. Unlike organic life, machine intelligence, built within silicon, needs protocols to coordinate and grow. And, like nature, these protocols should be open, permissionless, and neutral. Starting with compute hardware, the Gensyn protocol networks together the core resources required for machine intelligence to flourish alongside human intelligence.
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
