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About the job

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.

What You'll Do

- 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

What We'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

Nice to Have (But Not Required)

- 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)

Why This Role

- 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