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Rerun is seeking a hands-on engineer who works inside customer teams for several months to get their robot and sensor data flowing into Rerun.
A strong candidate has solid Python, real experience with large data systems, and the patience to ask good questions until the true problem is clear. Some travel to customer sites and coordination between two companies are expected, with a focus on keeping production pipelines healthy.
The role is a hands-on engineer who works inside customer teams for several months, getting their robot and sensor data flowing into Rerun and keeping production pipelines healthy. A strong candidate has solid Python, real experience with large data systems, and the patience to ask good questions until the true problem is clear. Comfort with ambiguity, clear coordination between two companies, and some travel to customer sites all matter.
Physical AI teams depend on huge amounts of camera, lidar and sensor data to train and test their models, so the tools that organize that data are becoming central to the work. AI tools can speed up writing pipeline code and debugging, but this job still rests on human judgment: understanding a customer's messy setup, deciding what to build, and earning trust while turning field problems into product feedback.
The role offers close exposure to how robotics and spatial AI teams manage the data behind model training, evaluation and debugging. Someone can prepare by building a small project that logs multimodal sensor data, such as video and point clouds, and queries it. Trying Rerun's open-source viewer on a public robotics dataset is a practical first step.
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
Rerun provides an open-source multimodal data logging, visualization, and query framework designed for robotics and spatial computing pipelines, according to the company.