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We’re looking for a Robotics Data Infrastructure Engineer to own and build the data systems that power Verne’s robots in the real world. This is a hands-on founding engineer role with true ownership and freedom — your work will directly impact robots performing customer-critical tasks every day. You will architect and deploy data pipelines on both AWS and edge devices, manage large-scale multi-modal datasets (images, video, time-series, text, etc.), and build the tooling that connects real-world robot data to training and evaluation workflows. You’ll work across the full robotics software stack, from ingesting sensor data and telemetry, to enabling large-scale policy learning pipelines that drive production robots. Beyond writing great code, you’ll help drive technical decisions, lead cross-functional efforts, and bridge robotics, machine learning, and product requirements into scalable, reliable systems.
Job Responsibility:
Build and own our data backbone on AWS: Design and run cloud + edge pipelines using services like IoT Core, S3, ECR, Batch, ECS/EKS, and Step Functions. Your work keeps robot data flowing reliably and cost-efficiently from the field into the lab
Develop on-device data systems: Build robust, fault-tolerant data capture on edge PCs using MCAP/Protobuf, with clean schema contracts, buffering, and resumable uploads to the cloud
Wrangle massive multimodal datasets: Organize and version millions of images, videos, time-series (robot state, force/torque), and annotations. Enforce metadata, retention, and access patterns that scale
Build MLOps and DataOps pipelines: Automate data validation, labeling, augmentation, and model training/evaluation using containerized jobs and orchestrators like Batch, Step Functions, Airflow, or Prefect
Ensure data quality and health: Create ingestion checks, schema validation, deduping, drift detection, and real-time alerting around data freshness and completeness
Build internal tools that unblock others: Develop UIs/CLIs for browsing data, launching jobs, tracking experiments, and debugging robots in the field. Integrate with tools like Foxglove
Work across teams: Partner with hardware, ML, and product to turn raw field data into smarter robots and real customer value—fast.
Requirements:
B.S., M.S., Ph.D. in computer science or related fields