# Research Member of Technical Staff- Training Systems at Rhoda AI Research Member of Technical Staff- Training Systems at Rhoda AI in Mountain View. Full-time, paying $200k–$300k. Posted September 18, 2026. - Company: Rhoda AI - Location: Mountain View - Job type: Full-time - Pay: $200k–$300k - Posted: 2026-09-18 - Apply: https://jobs.ashbyhq.com/rhoda-ai/643d697d-45c4-4505-8967-433df7873134/application - Source: https://jobmagic.ai/jobs/research-member-of-technical-staff-training-systems-rhoda-ai-2bx5xx ## Requirements - 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 ## Description 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. **What You'll DoOwn 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) - 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 approaches - Improve execution efficiency through communication scheduling and overlap, graph capture and execution optimization, and runtime-level improvements - Contribute to and extend training frameworks where needed **Make performance observable and measurable** - Establish source-of-truth performance metrics: step-time breakdowns, MFU / throughput / scaling efficiency - Build tools to identify bottlenecks quickly, track performance across model families, and compare scaling behavior across configurations - Develop regression detection: microbenchmarks, performance baselines, and automated detection of efficiency regressions **Partner deeply with researchers** - Work side-by-side with research scientists and research engineers — no silos - Translate model innovations into scalable, efficient implementations - Advise on training tradeoffs for robotics world models: long-horizon sequences, rollout/evaluation cadence, multimodal and variable-length data **Collaborate on cluster-level efficiency** - Work 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 behavior **What We'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 **Nice to Have (But Not Required)** - GPU kernel or compiler-level experience (CUDA, Triton, graph capture, operator fusion) - Experience with multimodal or video training (variable-length sequences, packing/bucketing) - Experience working on large-scale training frameworks or distributed runtimes - Familiarity with cluster topology, networking, and large-scale scheduling effects **Why This Role** - Direct leverage on research velocity — every efficiency gain you make accelerates model iteration across the entire research team - Own the scalability and performance of large-scale multimodal training for real-world embodied intelligence, not static benchmarks - Improvements you make compound across every training run the company executes — high ownership, high impact, small elite team