Job Description
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