Research Scientist / Engineer - Training Systems

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)

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)

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

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

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

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

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

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