Gradient Robotics

60 Gradient Robotics Jobs Hiring Near You

$140 - $190/hr

... robotics, and embodied intelligence. If you're excited about pushing the boundaries of what ... Experience with differentiable simulation or gradient-based system identification. * Experience ...

$149 - $275/hr

Collaborate with robotics engineers to transition algorithms from desktop and server-class systems ... gradient-based optimization, and hyperparameter tuning for large-scale image datasets (1 yr, 6 mos)

Showing results 41-60

Gradient Robotics Jobs Information

Infographic showing various job openings at Gradient Robotics in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Physical job distribution.

Research Member of Technical Staff - Training Platform

Rhoda AI

Mountain View, CA • On-site

Full-time

Re-posted 21 days ago


Job description

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