Software Engineer, Infrastructure at Imbue (formerly Generally Intelligent) (S17)
$170K - $350K  •  
Remote or San Francisco, CA / Remote (US)
1+ years
About Imbue (formerly Generally Intelligent)

Generally Intelligent is an AI research company. We help machines learn to understand the world the way humans do. Our mission is to build human-like general machine intelligence and make it safely accessible in order to foster a more abundant, equitable, and creative human society.

We take a first-principles approach to understanding the fundamentals of learning, starting with simple self-supervised networks solving early evolutionary problems, and increasing complexity incrementally.

About the role


At Imbue, we leverage large amounts of compute to make our small research team more effective. This role is about enabling and supporting those large-scale compute efforts and all of the other software infrastructure that goes into making research a pleasant, seamless experience for the rest of the team, especially as we scale to increasingly higher scale systems.

Example projects

• Build and manage wrapper tools that allow code written for single hosts to scale seamlessly to large GPU clusters.

• Debug distributed exceptions and improve our logging and tracing stack.

• Design improvements to systems that manage secrets, configurations, ongoing jobs, and other stateful components of our software.

• Search out and prototype possible additions to our software stack that can integrate and provide extra leverage to pain points in typical workflows.

• Dig deeply into open-source or third-party code, including C/C++ libraries, to add debugging information or patch in performance improvements.

• Work collaboratively with other members of the team to debug, provide guidance, and design software that is resilient to hardware and networking failures as well as third party tooling issues.

• In summary: make good abstractions so that other engineers on the team can work at a higher level and everything else works seamlessly.

You are

• A good software engineer. We are strong adherents of "infrastructure as code", so you will be writing (and reading) lots of Python, bash, etc.

• Passionate about enabling other engineers and creating good tooling for developers to interact with infrastructure in an automated way.

• Experienced enough with dev ops to be properly opinionated. You should understand the trade-offs of various approaches and technologies, and not be dogmatically tied to a single tool.

• Careful and detail oriented. We do science and care deeply about the correctness of the results. Our infrastructure reflects this--we highly value robustness and ensuring that systems run reliably and correctly.

Compensation and 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.

• Compensation packages are highly variable based on a variety of factors. If your salary requirements fall outside of the stated range, we still encourage you to apply. The range for this role is $170,000–$350,000 cash, $10,000–$2,000,000 in equity.

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

Imbue builds AI systems that reason and code, enabling AI agents to accomplish larger goals and safely work in the real world. We train our own foundation models optimized for reasoning and prototype agents on top of these models. By using these agents extensively, we gain insights into improving both the capabilities of the underlying models and the interaction design for agents.

We aim to rekindle the dream of the *personal* computer, where computers become truly intelligent tools that empower us, giving us freedom, dignity, and agency to pursue the things we love.


We are on the cutting edge of deep learning - implementing papers, running experiments, and designing architectures.

Our stack is Python, PyTorch, Weights & Biases, and home-grown infrastructure to run experiments across AWS/GCP and our own GPUs.

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