Sensor Fusion & Perception Engineer for Autonomous Vehicles

applied; Sunnyvale (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Applied Intuition, Inc. is seeking a Perception & Fusion engineer to develop real-time sensor fusion across autonomous platforms and domains.

You will design and integrate sensor fusion algorithms across EO, IR, lidar, radar, and RF, interfacing with perception, tracking, and autonomy teams. You will deploy ML models (Python, PyTorch, TensorFlow) on embedded Linux devices, using Docker and ONNX for portability.

Qualifications

• MS or PhD in Electrical Engineering, Computer Engineering, Robotic Engineering, Computer Science, Optimization, or equivalent OR 5+ years of relevant experience working with sensor algorithms, hardware, and HIL software integration.
• Experience with multiple sensor modalities (EO, IR, lidar, radar, sonar, acoustics, etc.) and core understanding of sensor physics.

Responsibilities

• Develop, integrate, and adapt cutting-edge AI/ML algorithms running on the perception autonomy stack.
• Create interfacing software to autonomously control sensors (EO, IR, acoustics, radar, RF).
• Collaborate across sensor, tracking, and autonomy teams for deployment in DoD testing and demonstrations.
• Deploy containerized solutions to embedded Linux devices and collect performance data.
• Interact with DoD customers to understand use cases and triage field needs.

Skills

Sensor fusion

ML deployment

Python

Pytorch

TensorFlow

Linux

Docker

C++

Data handling

Remote collaboration

Education

MS or PhD in Electrical Engineering/CS/Robotics

Tools

ONNX

CMake

Embedded Linux

Job description

Applied Intuition, Inc. is seeking a Perception & Fusion engineer to develop real-time sensor fusion across autonomous platforms and domains.

You will design and integrate sensor fusion algorithms across EO, IR, lidar, radar, and RF, interfacing with perception, tracking, and autonomy teams. You will deploy ML models (Python, PyTorch, TensorFlow) on embedded Linux devices, using Docker and ONNX for portability.