Senior V&V Engineer, Autonomous Fleet & Public Road Testing

Gatik

Santa Clara (CA)

Sur place

USD 160 000 - 260 000

Plein temps

14 jours+

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Résumé du poste

Gatik seeks a Senior V&V Engineer for Public Road & Fleet Testing to validate system behavior on live customer routes, assess map updates, and monitor safety metrics ensuring driverless deployments meet gates.

Based in Santa Clara, CA, this onsite role collaborates with Operations, Safety, and Engineering to analyze fleet data, investigate events, and provide actionable feedback to improve autonomy algorithms.

Qualifications

  • 5+ years in field validation, fleet testing, or systems engineering for autonomous vehicles or large-scale robotic fleets.
  • Proficient in Python, SQL, and data visualization/log analysis platforms (Jupyter, Grafana, Foxglove, MCAP).
  • Experience analyzing sensor logs (LiDAR, Radar, Camera, CAN) to triage autonomy failures.
  • Strong understanding of Operational Design Domains (ODDs) and fleet-wide safety monitoring.

Responsabilités

  • Define and execute on-road structured testing and shadow fleet evaluation programs for new software builds across commercial customer corridors.
  • Validate map updates and route configurations across customer ODDs prior to driverless sign-off.
  • Perform root-cause analysis on events, anomalies, near-misses, and unexpected vehicle behaviors using log visualization tools.

Connaissances

Python
SQL
Data Visualization
Log Analysis
ODDs
Fleet Testing
Autonomy

Formation

B.S./M.S. in Systems Engineering / Computer Science / Automotive Engineering / Data Analytics

Outils

Jupyter
Grafana
Foxglove
MCAP

Description du poste

Gatik seeks a Senior V&V Engineer for Public Road & Fleet Testing to validate system behavior on live customer routes, assess map updates, and monitor safety metrics ensuring driverless deployments meet gates.

Based in Santa Clara, CA, this onsite role collaborates with Operations, Safety, and Engineering to analyze fleet data, investigate events, and provide actionable feedback to improve autonomy algorithms.

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