Senior Software Engineer, ML Ops & Infrastructure
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The role
Design and implement scalable ML infrastructure to train, evaluate, and deploy deep learning models that feed a real-time robotic control stack.
Scale data loading and training across 1000+ GPUs, optimizing throughput and latency.
Build distributed data pipelines for robotics data, enabling efficient model training.
Develop APIs and tooling to let internal and external researchers integrate ML techniques, including open-source model contributions.
Optimize compute resource allocation (GPUs/TPUs) and orchestrate jobs on GKE to reduce cost and improve reliability.
Create model understanding and analysis tools to ensure reliability and traceability across the ML lifecycle.
Scale data loading and training across 1000+ GPUs, optimizing throughput and latency.
Build distributed data pipelines for robotics data, enabling efficient model training.
Develop APIs and tooling to let internal and external researchers integrate ML techniques, including open-source model contributions.
Optimize compute resource allocation (GPUs/TPUs) and orchestrate jobs on GKE to reduce cost and improve reliability.
Create model understanding and analysis tools to ensure reliability and traceability across the ML lifecycle.
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