Much of the work we do at Imbue is effectively pure software engineering. Our perspective is that even machine learning research ends up being about 90% software engineering, so even without any prior machine learning knowledge, there is plenty to contribute as a normal software engineer. Even most of our machine learning research tends towards the software engineering side of the spectrum, as we prefer to automate the types of work that academic researchers typically do (ex: tuning hyperparameters, experimenting with small variations in network architectures, etc).

• Create an automated service for iterating on network architectures. Most of the work is in defining and implementing the search space over network (which are, at the end of the day, just code that defines a graph of computations).

• Implement new tasks in Avalon and related environments. We are constantly extending our systems to add newer, more complex tasks on which to train more capable agents.

• Optimize existing agents and models to make them lower latency and higher throughput.

• Develop improved graphing, debugging, and error handling tools to investigate the myriad ways that neural networks and agents fail.

• 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.

• 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.

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).