Machine Learning Research Engineer (San Francisco)

San Francisco, CA /
Engineering /
Full-Time
Summary

In this role you’ll work with our researchers to do cutting-edge deep learning research—conducting experiments, creating infrastructure, and developing tooling & visualizations—with the goal of developing more human-like machine intelligence.

Note: This role requires being onsite in San Francisco. If you're remote, please take a look at our Machine Learning Engineer (Remote) role.

Example projects

Implement a self-supervised network using contrastive and reconstruction losses.
Create a library on top of PyTorch to enable efficient network architecture search.
Implement networks from newly published papers.
Run massively parallel experiments to understand all variants of an architecture.
Develop more realistic simulations for training our agents.
Create visualizations to help us deeply understand what our networks learn and why.

You are

Passionate about understanding the fundamentals of intelligence.
Very comfortable writing Python.
Excited to be a world-class ML engineer.
A fan of pair programming.
Passionate about engineering best practices.

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.

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 last for decades, and our backers include Y Combinator, researchers from OpenAI, Threshold, 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.