Member of Technical Staff (Intern)

Sakana AI is offering a research internship for exceptional students and early-career researchers who are excited about nature-inspired approaches to artificial intelligence. As a Member of Technical Staff (Intern), you will work directly with our research team — including scientists who co-invented the Transformer architecture — on cutting-edge projects in evolutionary computation, neural architecture search, model merging, and other biologically-inspired AI techniques.

During your internship, you will own a meaningful research project from conception through execution. This means designing experiments, implementing novel algorithms, running large-scale training on our GPU clusters, analyzing results rigorously, and writing up your findings. Past interns at similar labs have gone on to publish at top venues like NeurIPS, ICML, and ICLR, and we aim to provide the same opportunity. You will have access to significant compute resources, mentorship from leading researchers, and the freedom to explore unconventional ideas.

What makes interning at Sakana AI different from other AI labs is our research philosophy. We draw inspiration from natural systems — evolution, swarm intelligence, ecosystems — to develop AI techniques that are more robust, efficient, and creative than purely gradient-based approaches. If you are the kind of researcher who is curious about why ant colonies can solve routing problems or how evolution discovers solutions that no human designer would conceive, you will thrive here.

We are looking for highly motivated candidates with strong fundamentals in machine learning, a genuine passion for research, and the intellectual courage to explore ideas that the mainstream considers unusual. You should be comfortable working independently, have strong coding skills, and be excited about the possibility that the best AI techniques are still waiting to be discovered in nature.

Requirements:

• Currently pursuing or recently completed MS/PhD in Computer Science, Machine Learning, or a related quantitative field
• Strong fundamentals in machine learning and deep learning with coursework or research experience
• Proficiency in Python and PyTorch with the ability to implement research papers from scratch
• Experience designing and running research experiments with proper methodology and analysis
• At least one published paper or significant research project in ML/AI (workshop papers count)
• Strong mathematical foundations: linear algebra, optimization, probability, and information theory
• Interest in evolutionary computation, neuroevolution, model merging, or nature-inspired algorithms is highly valued