Invent, implement, and scale new machine learning (ML) algorithms, such as architectures, optimizers, training objectives, and recipes. Design and build automated research systems in which Gemini proposes, codes, and evaluates changes to its own training stack, moving toward a model that designs its successor. Serve as the final verifier for AI-proposed algorithm changes, reproduce results, design decisive ablations across model scales, rule out confounds such as evaluation leakage or unequal tuning budgets, and decide what moves forward.
Build and own lean, high-performance training and evaluation code (JAX on Tensor Processing Units, with custom kernels where needed) allowing researchers and AI agents can test ideas at meaningful scale. Partner with Gemini pre-training, post-training, and infrastructure teams to land validated improvements in production models, and share results through internal talks.
Job description provided by Google.
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