Research Member of Technical Staff - Training Platform

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 $400M 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 utilization, gradient statistics, and anomaly detection

• Optimize and automate the research iteration loop from experiment launch to results analysis

• Manage job scheduling and cluster utilization for efficient use of GPU compute

• Build internal tooling and interfaces that help researchers move faster

• Collaborate with training systems, data infrastructure, and research teams to support their platform needs

• Strong software engineering skills with experience in MLOps or ML platform engineering

• Familiarity with distributed training frameworks (PyTorch DDP, FSDP, Deep Speed, 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)

• Familiarity with experiment tracking tools (Weights & Biases, MLflow, or custom solutions)

• Experience supporting large model training pipelines (LLMs, VLMs, or video models)

• Understanding of parallelism strategies and how they affect training efficiency and debugging

• Experience with cloud-based training infrastructure (AWS, GCP, or Azure)

• Your platform is the daily tool every researcher and engineer uses to train models

• Improvements to training velocity and reliability compound across every experiment the team runs

• High visibility with direct feedback from researchers and ML engineers

• Build systems that scale from today's models to future frontier training runs