Research Engineer - Training Platform

At Rhoda AI, we're building the full-stack foundation for the next generation of humanoid robots — from high-performance, software-defined hardware to the foundational models and video world models that control it. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling scenarios unseen in training. We work at the intersection of large-scale learning, robotics, and systems, with a research team that includes researchers from Stanford, Berkeley, Harvard, and beyond. We're not building a feature; we're building a new computing platform for physical work — and with over $400M raised, we're investing aggressively in the R&D, hardware development, and manufacturing scale-up to make that 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