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The role involves designing and maintaining large-scale ML infrastructure, optimizing distributed training systems, and enhancing computing performance for model training.

Own continuous learning for deployed robot fleets: build pipelines to convert deployment data into fine-tuning and policy updates, mine multimodal fleet data for failures and drift, apply RL and reward modeling, create automated real-time monitoring and evaluation, and partner across teams to ship measurable improvements to production robots.

The role involves applied research and engineering for deploying AI in robotics, focusing on infrastructure design, fleet management, and software stability.