Machine Learning Researcher - Springtail
Conducts fundamental research on data-efficient ML architectures, including bootstrapped program synthesis and self-synthesizing learning systems. Requires Master's in ML/math, PyTorch fluency, and research experience.
About the job
Responsibilities
- Hypothesize, test, and refine means of improving generalization performance of common architectural elements, including different forms of attention. This includes devising controlled datasets to elucidate e.g. learning order & learned representations.
- Think both mathematically and empirically about problems of runtime inference in gradient-trained networks, with an eye to the extensive literature on statistical learning and an open mind to the many forms of constrained optimization.
- Contribute to a well-documented and well-instrumented code base that is performant where necessary yet expeditious where experimental throughput demands.
Qualifications and Experience
- Masters or equivalent in machine learning, mathematics, or equivalent fields (strong candidates from neuroscience are encouraged to apply).
- Fluency with Pytorch, and familiarity with JAX, CUDA, and/or Triton + their open-source ecosystems.
- Demonstrated ability do fundamental research.
- Demonstrated ability to work in teams.
Skills
PyTorch, JAX, CUDA, Triton, Attention Mechanisms, Program Synthesis, Gradient-Trained Networks, Statistical Learning, Constrained Optimization
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