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Clera

Robotics Data Infrastructure Engineer

Los Angeles (Remote)fulltimemidAdded today

About this role

Build scalable data pipelines and storage systems for robot telemetry and sensor data at an early-stage robotics company. You'll enable ML and engineering teams to process and analyze high-volume sensor streams from cameras, LiDAR, IMUs, and other sources while ensuring data quality and real-time performance.

What you'll do

  • Design and build scalable pipelines to ingest and process robot telemetry and multi-modal sensor data
  • Implement storage solutions and schemas supporting analytics, model training, and data replay workflows
  • Ensure data quality, validation, and lineage across ingestion and transformation stages
  • Optimize latency and throughput for real-time and batch processing use cases
  • Instrument observability, monitoring, and alerting for data infrastructure and flows
  • Productionize ETL/ELT workflows with CI/CD, automated testing, and incident resolution

What they're looking for

  • Python and/or C++
  • Cloud platforms (AWS or GCP)
  • Kafka or Pub/Sub messaging
  • Spark or Flink distributed processing
  • Docker and Kubernetes containerization
  • Time-series databases and telemetry systems
  • Airflow or workflow orchestration
  • Data pipeline design and distributed systems fundamentals

Benefits

  • Equity participation in early-stage company
  • Competitive compensation commensurate with experience
  • Full-time on-site role in Los Angeles
  • Cross-functional collaboration with robotics engineers and data scientists
  • High-impact work on frontier robotics and physical AI
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Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

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Likely interview questions

  • Walk us through a robotics sensor data pipeline you've built—what challenges did you encounter with scale or multi-modal data types?
  • How would you design a system to ingest and replay sensor data (camera, LiDAR, IMU) for both training and real-time analytics?
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