Systems Engineer

Summary

As a systems engineer, you’ll work on pioneering machine learning infrastructure that enables running large numbers of experiments in parallel across local and cloud GPUs, extremely fast training, and guarantees that we can trust experiment results. This allows us to do actual science to understand, from first principles, how to build human-like artificial general intelligence.

You’ll also play a role in open sourcing infrastructure for the machine learning community.

No machine learning experience is required.

Example projects

• Abstracting cloud and physical GPU resources.

• Implementing a caching system for models and datasets.

• Profiling and optimizing third-party C++ code.

• Using eBPF for continuous system profiling.

• Turning man pages into bug fixes.

• Creating observability tools for distributed systems.

• Writing shaders, learning about video encoding on the GPU, etc.

You are

• Very comfortable writing Python and reading bash.

• Obsessive about deeply understanding how systems work.

• Happy to debug any weird problem all the way down.

• Familiar with Docker, cloud services, physical servers, systems internals.

• Excited to work on open source code.

• Passionate about engineering best practices.

• Self-directed and independent.

• Excellent at getting things done.

Benefits

• Work directly on creating software with human-like intelligence.

• Generous compensation, equity, and benefits.

• Spend time learning and pairing with world-class engineers working across diverse problems who are excited share their knowledge to get you up to speed.

For full-time team members onsite in San Francisco:

• Actively co-create and participate in a positive, intentional team culture.

• Frequent team events, dinners, off-sites, and hanging out.

• $20K+ yearly budget for self-improvement: coaching, courses, conferences, etc.

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.