Perception Data Engine Architect for Autonomous Robotics

Watney Robotics Inc

San Francisco (CA)

On-site

USD 180,000 - 260,000

Full time

14 days+

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Job summary

Watney Robotics Inc. is seeking a backend/data infrastructure engineer to design scalable data engines for autonomous fleets. You will architect off-robot pipelines, ingest petabyte-scale video and telemetry, and support auto-annotation and localization tasks.

You’ll build deterministic evaluation platforms, multimodal data fusion pipelines, and telemetry-driven observability to ensure robust perception models before deployment.

Qualifications

  • Experience building high-throughput data platforms for autonomous robotics.
  • Strong background in scalable data pipelines and storage for multimodal media.
  • Experience with testing frameworks and metric tracking in complex software.

Responsibilities

  • Own the Perception Data Engine: architect scalable pipelines for petabyte-scale video and sensor streams for auto-annotation and localization.
  • Build Continuous Evaluation Infrastructure: deterministic simulation and regression testing before edge deployment.
  • Scale Annotation & Feature Extraction: pipelines for multimodal data fusion and automated labeling.
  • Drive Quality Metrics & Monitoring: telemetry systems to track regression and data drift across environments.

Skills

High-throughput data platforms
Distributed streaming systems
Infrastructure for autonomous robotics
Closed-loop testing infrastructure
Regression frameworks
Metric tracking engines
Geometric computer vision
3D spatial data handling
Metadata orchestration

Job description

Watney Robotics Inc. is seeking a backend/data infrastructure engineer to design scalable data engines for autonomous fleets. You will architect off-robot pipelines, ingest petabyte-scale video and telemetry, and support auto-annotation and localization tasks.

You’ll build deterministic evaluation platforms, multimodal data fusion pipelines, and telemetry-driven observability to ensure robust perception models before deployment.

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