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ML Research Engineer, Training

Weave Robotics· Engineering
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San Francisco, CAFull-timePosted 2w ago

About the role

Weave Robotics is seeking an ML Research Engineer, Training to own and optimize the training stack for real-world robot data, enabling fast iteration from research to production in home robotics.

Responsibilities

  • Build training stack end to end: distributed training, data loading, checkpointing, run orchestration, experiment tracking.
  • Large-scale data handling: develop high-throughput data ingestion, transformation, and storage systems for terabytes of multimodal robot data (video, proprioception, sensor streams).
  • Optimize research productivity: grow a reproducible, scalable, and debuggable codebase for experiments.
  • Sampling and curation: drive sampling and curation decisions that influence model behavior.
  • Make runs fast and honest: profile and fix throughput bottlenecks, address loss spikes and data bugs, ensure reproducibility from data loading to GPU kernels.
  • From research to production: turn research prototypes into scalable infrastructure for the team to train on.

Qualifications

  • ML system expertise: deep PyTorch or JAX experience, including multi-node distributed training (FSDP, DDP, or equivalent) on real workloads.
  • Performance engineering: experience profiling and optimizing GPU utilization, data pipelines, I/O bottlenecks, memory usage, and distributed training performance; CUDA profiling tools (Nsight) and NCCL tuning.
  • Training run judgement: ability to read loss curves, diagnose instability vs data bugs, know when to terminate a run.

Skills

Required:PyTorch or JAXMulti-node distributed trainingGPU profiling and optimization
Last updated Oct 7, 2026. Interested? Apply on the company site.
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About Weave Robotics

Personal robots for the home--that ship in 2025

Making the world’s first personal robot that's built for the home. Our robot, Isaac, will autonomously tidy up endless messes, fold laundry, and care for your home while you’re away, and we’re shipping our first 30 in fall of 2025.

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