Mercor
Software Engineer, Robotics Data
About this role
Build petabyte-scale data pipelines that ingest, validate, and package multi-sensor robotics data from the field for frontier AI labs. Own the end-to-end infrastructure transforming raw video, depth, and inertial streams into production-ready datasets with automated QC and privacy protection.
What you'll do
- Design and build end-to-end sensor data ingestion pipelines from field devices through segmentation, pre-labeling, QC, and delivery
- Develop automated quality control systems validating timing, synchronization, calibration, and sensor continuity at scale
- Create dataset schemas, versioning, and provenance tracking for full traceability across all deliverables
- Build shared processing components for privacy redaction, transcription, encoding, and format packaging
- Integrate vision-language model pre-labeling into production workflows while maintaining debuggability and human oversight
- Operate systems handling thousands of concurrent data collectors generating petabytes of multi-modal sensor data
What they're looking for
- Python
- Data pipeline architecture and ownership
- Video and sensor data processing at scale
- AWS cloud infrastructure
- Distributed batch processing
- Data validation and quality assurance
- Robotics data formats (MCAP, ROS bags, protobuf)
- Computer vision and multimodal ML
Benefits
- Bi-annual performance bonus
- Equity grant vested over 4 years
- Up to $15k relocation bonus
- $10k housing bonus (within 0.5 miles of office)
- $1.5k monthly meal stipend, health/dental/vision insurance, gym membership
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Mercor
Mercor builds a marketplace platform connecting expert talent to AI opportunities, supported by identity infrastructure, matching algorithms, and internal tools for data management. The company is hiring Software Engineers, Machine Learning Engineers, Fullstack Engineers, and Security Engineers to develop backend systems, ML models, cloud infrastructure, and distributed platforms.
- Website
- mercor.io
Likely interview questions
- Walk us through the largest data pipeline you've owned—what was the volume, latency requirements, and biggest scaling challenge you faced?
- How would you design automated QC for multi-sensor data when you can't manually inspect every recording?