Machine Learning Engineer (Remote or San Francisco)

REMOTE OR SAN FRANCISCO, CA /ENGINEERING /FULL-TIME/ REMOTE

Summary

As a remote machine learning engineer, you’ll work very closely with a senior member of our research team on cutting-edge deep learning research, infrastructure, and tooling towards the goal of creating general human-like machine intelligence.

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.

• Open source internal tools.

• Implement networks from newly published papers.

• Work on tools for simple distributed parallel training of deep neural networks.

• Develop more realistic simulations for training our agents.

• Design automated methods and tools to prevent common issues with neural network training (e.g. overfitting, vanishing gradients, dead ReLUs, etc).

• Create visualizations to help us deeply understand what our networks learn and why.

You are

• Very comfortable writing Python.

• Familiar with PyTorch and training deep neural networks.

• 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

• Very generous compensation

• Flexible working hours

• Work remotely

• Time and budget for learning and self improvement

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.