Deep expertise and intuition for solving machine learning problems and training models
Experience with training on large-scale (multi-node) GPU clusters
Deep understanding of model training pipelines – including model/data parallelism, distributed optimizers, etc.
Strong grasp of proper experimental methodology for running rigorous ablations and other hypothesis testing
Understanding of large-scale, highly parallel data processing pipelines
High proficiency with PyTorch and Python.
Strong ability to dive into large pre-existing codebases and rapidly get up to speed
Published machine learning research in well-respected venues is a plus
Postgraduate degree in a scientific subject (Computer Science, EE/EECS, Math, Physics)
