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Mercor is an AI data company on a mission to organize human intelligence to power the AI economy. The company operates a platform where millions of domain experts train frontier AI models, and is expanding into physical-world data collection for robotics and autonomous systems. Mercor is a profitable Series C company valued at $10 billion, requiring in-person work five days per week in San Francisco, NYC, or London.
In this role, you'll build the data backbone for physical AI by designing and owning end-to-end sensor data pipelines. You'll ingest raw multi-sensor capture from the field (video, depth, inertial, audio) and transform it into validated, privacy-safe, delivery-ready datasets for frontier AI labs. Your responsibilities include building ingestion systems, designing automated quality control at scale, establishing dataset schemas with full provenance tracking, creating shared processing components (privacy redaction, transcription, encoding, format packaging), and integrating VLM-assisted pre-labeling into production workflows.
This is a high-ownership role in a strategically central product area. The data you pipeline produces directly shapes the models that frontier labs train on, making your work directly impactful. You'll work with real physical-world scale—inputs from devices operated globally for thousands of hours—requiring systems that handle petabyte-scale data across thousands of concurrent collectors.
You should have strong production backend and data engineering experience with high-volume pipelines, hands-on experience processing video or sensor data at scale (large binary formats, streaming ingestion, distributed batch processing, object storage), fluency in Python and AWS, and genuine data intuition. Nice-to-have skills include robotics data formats (MCAP, ROS bags, protobuf, Foxglove), camera geometry, multi-sensor calibration, computer vision, or prior work on data engines for autonomous vehicles or robotics.
