Research Member of Technical Staff- Training Systems

We're looking for a Staff / Principal ML Training Systems Engineer to own training systems performance end-to-end. You will define how our models train at scale — driving efficiency, scalability, and correctness across large-scale multimodal training. This is a core systems role, not infrastructure support. Your work directly determines how efficiently we use compute, how well models scale across thousands of GPUs, and how quickly research can iterate.

Own training performance end-to-end

• Diagnose and improve performance of large-scale multimodal training (vision, video, proprioception, actions, language)
• Build systematic performance attribution: step-time decomposition (compute vs communication vs input pipeline), scaling curves across cluster sizes, and bottleneck identification and prioritization
• Drive measurable gains in:
• Distributed efficiency (comm/compute overlap, bucketization, topology-aware mapping, parallelism strategies)