Lead Sensor Validation for Self-Driving Systems

Applied Intuition; Sunnyvale (CA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Applied Intuition, Inc. is seeking a skilled engineer to lead sensor validation for SDS products across Automotive, Trucking, and Construction & Mining industries.

The role focuses on validating Lidar, Radar, Camera, and Ultrasonic sensors to ensure performance across diverse environments. You will develop ML and physical models to improve synthetic simulations, perform physical sensor validation against simulated data, and collaborate with engineering teams to bridge real and simulated

Qualifications

• Bachelor's degree in Electrical/Mechanical/Robotics Engineering or related field.
• 3+ years of experience in sensor validation or related work.
• Proficiency in Python for data analysis and automation.
• Experience with validation/characterization of Cameras, Lidar, or Radar sensors.

Responsibilities

• Lead sensor validation and characterization for SDS products across Automotive, Trucking, and Construction & Mining industries.
• Execute validation protocols for Lidar, Radar, Camera, and Ultrasonic sensors across diverse environments.
• Improve synthetic simulations by developing ML and physical models of sensing systems and environments.
• Conduct physical sensor performance validation, comparing hardware data with simulated models.
• Collaborate with engineering teams to align physical data with synthetic models for high-accuracy perception.

Skills

Python

Sensor validation

Linux/Unix

Sensor driver

Education

Bachelor's degree in Electrical Engineering, Mechanical Engineering, Robotics Engineering

Tools

UDP data parsing

Job description

Applied Intuition, Inc. is seeking a skilled engineer to lead sensor validation for SDS products across Automotive, Trucking, and Construction & Mining industries.

The role focuses on validating Lidar, Radar, Camera, and Ultrasonic sensors to ensure performance across diverse environments. You will develop ML and physical models to improve synthetic simulations, perform physical sensor validation against simulated data, and collaborate with engineering teams to bridge real and simulated