Iteration speed is all you need. In frontier AI, the fastest team from hypothesis to trustworthy evidence wins. That takes three things: Scale. More data, compute, and experiments should be a config change — not an infra rebuild. Speed. Processing, training, deployment, and evaluation have to be fast enough that every result actually shapes the next decision. Attention. Researchers' time should go to ideas and judgment — not moving data, hunting GPUs, recovering jobs, or reconstructing results. Knowing that more data and compute produce better models isn't enough — you have to learn fast enough to shape the next run. Robotics raises the bar further. Collecting data, training a policy, and shipping a demo is step one. The hard part is doing that on repeat — across massive multimodal datasets, multiple robot configs, distributed compute, and rigorous real-world evaluation. Robot data isn't just text or images. It's synchronized video, actions, proprioception, poses, language, sensors, hardware state, and physical outcomes. And training is only the middle of the loop: the model has to return to the robot, the robot has to generate evidence, and that evidence has to drive the next experiment. At Dyna Robotics, we're hiring for exactly this: ML Infrastructure Engineer, Training Build the research operating system behind our models — distributed training, high-throughput data loading, scheduling, recovery, reproducibility, serving, and evaluation. Software Engineer, Data Infrastructure Turn massive robot experience into reliable, inspectable, training-ready data. These aren't support roles. They set the ceiling on how fast we scale, learn, and improve. A demo proves a possibility. Infrastructure compounds real-world progress. If you are enthusiastic about this, please contact me or directly apply! Let's land cutting-edge research into the real world together!