Our mission at OpenAI is to discover and enact the path to safe, beneficial AGI. To do this, we believe that many technical breakthroughs are needed in generative modeling, reinforcement learning, large scale optimization, active learning, among other topics.

As a Software Engineer, you will build AI systems that can perform previously impossible tasks or achieve outstanding levels of performance. This requires good engineering (for example designing, implementing, and optimizing state-of-the-art AI models), writing bug-free machine learning code (surprisingly difficult!), and building the science behind the algorithms employed. In all the projects this role pursues, the ultimate goal is to push the field forward.

The Research Acceleration team builds high-quality research tools and frameworks to increase research productivity across OpenAI, with the goal of accelerating progress towards AGI. For example, we develop Triton, a language and compiler for writing custom GPU kernels. The aim of Triton is to provide an open-source environment to write fast code at higher productivity than CUDA. 

We frequently collaborate with other teams to speed up the development of new state-of-the-art capabilities. For example, we recently collaborated with our Codegen research team on the Codex model, which can generate code in Python and many other languages.

Do you love research tools, compilers, and collaborating on cutting-edge AI models? If so, this role is for you! We are looking for people who are self-directed and enjoy determining the most meaningful problem to solve to accelerate our research.

We're looking for a track record of:

• 3+ years of relevant engineering experience
• Experience writing code for GPUs such as CUDA/OpenCL kernels
• Owning problems end-to-end, with a willingness to pick up whatever knowledge is missing to get the job done
• Bonus: contributions to an AI framework such as PyTorch or Tensorflow