Rhoda AI develops video-predictive control foundation models that enable robots to autonomously adapt to real-world manufacturing and logistics environments.
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