Redefining Robotic Intelligence. Backed by Khosla.
About the role
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.
What they're looking for
- Proven track record improving large-scale distributed training performance
- Deep hands-on experience with modern ML stacks (PyTorch required, JAX a plus)
- Strong understanding of data / tensor / pipeline parallelism, sharded training (FSDP / ZeRO-style), communication patterns and overlap strategies, and scaling behavior across large GPU clusters
- Strong systems intuition — ability to reason across compute, communication, and memory bottlenecks
- Exceptional debugging and measurement ability: turn "training is slow" into clear bottlenecks, experiments, and validated improvements
- High ownership mindset and comfort in a fast-moving environment
More about this role
At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.
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.
Diagnose and improve performance of large-scale multimodal training (vision, video, proprioception,...
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