Research Member of Technical Staff - Training Platform

Research Engineer

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 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