Research Member of Technical Staff - Training Systems

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.

Responsibilities

• 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)
• 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

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
• 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