Research Member of Technical Staff - Training Platform
At a glance
Senior Software Engineering role at Rhoda AI. Mountain View · full-time · $200,000–$300,000 base.
techroles summary, based on the employer's posting.
What you'll do
- Build and maintain systems that coordinate distributed model training across GPU clusters
- Create tools for setting up experiments, tracking runs, preserving reproducibility and managing artifacts
- Surface training health through loss curves, compute use, gradient statistics and anomaly alerts
- Streamline the path from launching an experiment to analyzing its results, while improving job scheduling and cluster utilization
- Develop researcher-facing tools and coordinate with training systems, data infrastructure and research teams
What you bring
- Strong software engineering ability, with background in MLOps or machine-learning platform work
- Experience with distributed training frameworks such as PyTorch DDP, FSDP or another listed option
- Experience building systems for experiment tracking, reproducibility and artifact handling
- Comfort operating GPU clusters, using Slurm, Kubernetes or a comparable platform
Who this fits
This senior engineering role suits someone focused on the tools and infrastructure researchers use to train models. You’ll work closely with research, training systems and data infrastructure teams. The role is based in Mountain View.
From the employer
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
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Software Engineering$200k+
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