Data & ML Infrastructure
Data pipelines, simulation & MLOps — the data stack behind physical AI.
Why it matters in robotics
As robotics shifts from hand-tuned controllers to learning-based, fleet-scale systems, companies increasingly hire for the infrastructure that makes scaled robot learning possible: data pipelines, simulation, and MLOps. Interviewers probe whether you can reason about the full loop, collecting and standardizing heterogeneous robot data (ROS2 bags, RLDS/LeRobot datasets), generating training data in simulation (Isaac Sim/Lab, MuJoCo, Gazebo) and crossing the sim-to-real gap, then deploying, monitoring, and OTA-updating policies across a fleet. Common questions include how to design a multi-robot data format, why GPU-parallel simulation changes RL economics, how domain randomization mitigates the sim-to-real gap, and how to make training and evaluation reproducible. System-design rounds may ask you to architect a teleoperation-to-training data flywheel or a fleet observability stack. Strong candidates connect concrete tooling choices to throughput, cost, reproducibility, and safety tradeoffs rather than treating infra as glue code.
Application focus
The same topic, tailored to the robot you're building. Your choice is remembered across the roadmap and every topic.
At a glance
The scaled robot-learning data flywheel: collect heterogeneous data, standardize it, train and evaluate policies, deploy to the fleet, and feed monitored real-world experience back into collection.
What to study
- ✓Robot data pipelines and dataset formats: ROS2 bags (rosbag2/MCAP) for raw logging, and standardized training formats like RLDS and LeRobotDataset (Parquet+MP4); how Open X-Embodiment pooled 60+ datasets across 22 embodiments into one schema.
- ✓Simulation platforms and when to use each: GPU-parallel Isaac Sim/Isaac Lab for massively parallel RL, MuJoCo for fast contact-rich dynamics, Gazebo for ROS-integrated robot/sensor simulation.
- ✓Sim-to-real transfer: domain randomization (dynamics, textures, lighting), system identification, and how parallel-environment RL scaling changes wall-clock training cost and data economics.
- ✓Fleet MLOps for robotics: teleoperation and data-flywheel collection, OTA policy deployment, observability/logging, and reproducible training plus rigorous (often real-world) evaluation.
Study by time budget
Pick the path that fits the time you have before your interview.