Research Member of Technical Staff- Training Systems

Research Member of Technical Staff- Training Systems LocationMountain ViewEmployment TypeFull timeDepartmentResearchCompensation$200K – $300K • Offers EquityAt 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.What You'll DoOwn training performance end-to-endDiagnose 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 prioritizationDrive measurable gains in:Distributed efficiency (comm/compute overlap, bucketization, topology-aware mapping, parallelism strategies)Compute efficiency (kernel hotspots, operator fusion, attention optimization, framework/runtime overhead)Memory efficiency (activation checkpointing, sequence packing/bucketing, fragmentation reduction)Design training systems (not just tune them)Define and evolve parallelism strategies: data / tensor / pipeline / sharding / hybrid approachesImprove execution efficiency through communication scheduling and overlap, graph capture and execution optimization, and runtime-level improvementsContribute to and extend training frameworks where neededMake performance observable and measurableEstablish source-of-truth performance metrics: step-time breakdowns, MFU / throughput / scaling efficiencyBuild tools to identify bottlenecks quickly, track performance across model families, and compare scaling behavior across configurationsDevelop regression detection: microbenchmarks, performance baselines, and automated detection of efficiency regressionsPartner deeply with researchersWork side-by-side with research scientists and research engineers — no silosTranslate model innovations into scalable, efficient implementationsAdvise on training tradeoffs for robotics world models: long-horizon sequences, rollout/evaluation cadence, multimodal and variable-length dataCollaborate on cluster-level efficiencyWork with infrastructure/SRE teams to improve utilization across large distributed jobs, impact of network and collective performance on training, and topology-aware job placement and scaling behaviorWhat We're Looking ForProven track record improving large-scale distributed training performanceDeep 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 clustersStrong systems intuition — ability to reason across compute, communication, and memory bottlenecksExceptional debugging and measurement ability: turn "training is slow" into clear bottlenecks, experiments, and validated improvementsHigh ownership mindset and comfort in a fast-moving environmentNice to Have (But Not Required)GPU kernel or compiler-level experience (CUDA,...