Machine Learning Research Scientist

Our approaches to solving the above problems are inspired by cognitive science and neuroscience. To measure our progress, we are implementing reinforcement learning tasks where humans currently do much better than state-of-the-art AI.

We are looking for a Machine Learning Research Scientist to join the Obelisk team. You will collaborate with engineers and other scientists to generate hypotheses, design experiments, and implement models and algorithms for making progress on the above-mentioned problems.

You will be required to be in the office in Berkeley at least 2 days per week because we find it significantly improves team cohesion and makes collaboration easier.

• Invent and implement new model architectures or learning algorithms for improving performance on tasks we care about.
• Review relevant literature to find techniques we can build upon.
• Guide the development of tasks that are useful for training and testing our systems.
• Rigorously document your experiments.
• Clearly communicate your results internally verbally and in writing.

• PhD in AI, ML, computer science, cognitive science, or related, or extensive (5+ years) practical experience which demonstrates similar technical expertise, independence, grit and creativity.
• Publications, work experience, or other demonstrated involvement in any of the areas listed in What we're looking for above.

• Significant familiarity with some technical field outside of machine learning.
• Experience designing and training deep learning models from scratch to solve new problems.

• We're building environments on top of Minetest, an open source game engine implemented in C++, with a Lua API for building games.
• Our models are written using PyTorch (though we're open to using Jax).
• We have our own cluster of computers with Nvidia GPUs, managed by Kubernetes.

• Machine Learning Research Engineer - more emphasis on running and optimizing experiments, requires less AI theory knowledge than this role.

• Plenty of funding and computers.
• Trying to advance the state of the art in AI.
• Smaller focus. Other places (e.g., Deep Mind) are doing research into lots of problems simultaneously, or are doing research and building products (e.g. Anthropic). We are completely focused on a small set of problems.
• Smaller team. This has benefits and disadvantages, but a huge advantage is less communication overhead and bureaucracy. This makes work faster and more fun.
• No outside funding means there’s no pressure to chase trends or make products.