Startups · AI

Research Member of Technical Staff - Training Platform

Rhoda · Mountain View · On-site

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About Rhoda

Redefining Robotic Intelligence. Backed by Khosla.

About the role

Build and maintain training orchestration systems for large-scale distributed model training across GPU clusters Develop experiment management tooling: job configuration, tracking, reproducibility, and artifact management

What they're looking for

  • Strong software engineering skills with experience in MLOps or ML platform engineering
  • Familiarity with distributed training frameworks (PyTorch DDP, FSDP, DeepSpeed, Megatron, or similar)
  • Experience building experiment tracking, reproducibility, and artifact management systems
  • Comfortable managing and operating GPU cluster environments (Slurm, Kubernetes, or similar)
  • Strong reliability engineering instincts: monitoring, alerting, and failure recovery
  • Experience with training orchestration tools (Slurm, Ray, Kubernetes, or similar schedulers)
More about this role

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 Research Engineer to build and maintain the training platform that powers our model development — experiment orchestration, job management, observability, and the tooling that lets researchers move from idea to result as fast as possible.

Build and maintain training orchestration systems for large-scale distributed model training across GPU clusters

Develop experiment management tooling: job configuration, tracking, reproducibility, and artifact management

Build observability infrastructure for training runs: loss curves, compute...

Read the full posting on Rhoda's site ↗

Research

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