Post-Training Research Engineer

We don’t have a rigid set of skills, but here’s some of what we’re looking for

• A deep understanding of modern ML techniques and tools for training transformers
• Advanced experience in a tensor/array computation library like PyTorch, TensorFlow, Jax, or similar
• A detailed understanding of transformer training parallelism strategies like data parallelism, sharded data parallelism, tensor parallelism, pipeline parallelism, context parallelism
• The experience and knowledge to profile and improve the performance of a distributed GPU program in PyTorch or a similar library
• The ability to perform roofline analysis on a transformer training setup
• A willingness to dive into messy problems, work with researchers, derive specifications by asking important questions, and execute
• Familiarity with HPC and distributed computing platforms like Slurm, Ray, Kubernetes, and Dask
• Familiarity with cluster networking technology like Infiniband, RoCE, GPUDirect
• Solid fundamentals in operating systems concepts like processes, files, kernel drivers, containerisation, and networking protocols
• A sense of creativity and willingness to ask difficult questions about our approach, assumptions, and tooling choices