Theory Engineer

Remote and San Francisco, CA /
Engineering /
Full-Time
Summary

One of the main directions of research we care about at Generally Intelligent is developing better theoretical models and understanding of learning, optimization, memory, and agents. In particular, we're interested in the intersection of this theoretical knowledge and what that means for our day-to-day engineering of more general agents. This position is about developing the software to support those experiments and explorations.


Example projects

Creating a service to compute convolutional neural tangent kernel matrices for use by both internal and external users
Investigating the ways in which hyperparameters transfer to larger scale models (ex: extending μTransfer to other models and parameters)
Creating toy examples, graphs, and visualizations to help understand the learning-time mechanics of self-supervised networks like BYOL, VICReg, and others.
Running thousands of experiments to empirically determine the optimal learning rate schedule for a network.


You are

A good programmer. Much of the work will require quickly putting together experiments and one-off visualizations. It will require both attention to detail, and speed.
Mathematically inclined. Your work will benefit from a broad knowledge of (and comfort with) mathematics. In particular, knowledge of kernels, matrices, information theory, and related topics will be helpful.
Passionate about science and understanding how deep learning actually works. You should be driven to really deeply understand these topics and be unsatisfied with hand-wavy explanations.
A good communicator. You will need to communicate your results and questions to both the rest of the team, and the broader world via papers and blog posts, both of which require an ability to explain complex ideas in a succinct, easy-to-understand manner.


Benefits

Work directly on creating software with human-like intelligence.
Generous compensation, equity, and benefits.
$20K+ yearly budget for self-improvement: coaching, courses, conferences, etc.
Actively co-create and participate in a positive, intentional team culture.
Spend time learning, reading papers, and deeply understanding prior work.
Frequent team events, dinners, off-sites, and hanging out.


How to apply

All submissions are reviewed by a person, so we encourage you to include notes on why you're interested in working with us. If you have any other work that you can showcase (open source code, side projects, etc.), certainly include it! We know that talent comes from many backgrounds, and we aim to build a team with diverse skillsets that spike strongly in different areas.

We try to reply either way within a week or two at most (usually much sooner).

Learn more about our full interview process here.


About us

We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.

We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research.

Our research is focused primarily on self-supervised and generative video and audio models. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.