Applied Intuition

Perception & Fusion Engineer

Poway, CA$150k–$220kfulltimemidAdded today

About this role

Applied Intuition seeks a Perception & Fusion Engineer to develop and integrate real-time sensor fusion algorithms for autonomous vehicles across defense and commercial domains. You'll design software that fuses data from multiple sensor modalities (EO, IR, radar, acoustics, etc.), deploy containerized solutions to embedded systems, and support field demonstrations with DoD customers.

What you'll do

Develop and integrate AI/ML algorithms for multi-sensor perception stacks across heterogeneous autonomous platforms
Design autonomous sensor control software to optimally fuse EO, IR, radar, acoustics, and RF sensor data
Deploy containerized solutions to embedded Linux devices with performance profiling and testing
Collaborate with sensor, tracking, and autonomy teams for seamless integration and field deployment
Interact with DoD customers to understand requirements, triage issues, and deliver demonstrations
Execute simulation tooling for post-processing field test analysis and drive improvement plans

What they're looking for

Sensor fusion and multi-sensor algorithms
ML/AI deployment (Python, PyTorch, TensorFlow, ONNX, C++)
Sensor physics and control parameters
State estimation and tracking algorithms
Linux and Docker containerization
Hardware-in-the-loop (HIL) software integration
Large dataset processing and analysis
Remote software development

Benefits

Work on cutting-edge autonomous vehicle technology for defense and commercial sectors
Collaborate with top-20 global automakers and U.S. military partners
$15 billion company with strong market position
Exposure to diverse sensor modalities and multi-domain platforms
Flexible work arrangements with occasional remote options
Relocation assistance available to Poway HQ

Likely interview questions

Can you describe your experience working with multiple sensor modalities and the challenges you've faced fusing data from different sensor types?
Walk us through a project where you deployed ML models to embedded systems—what frameworks did you use and what were the performance constraints?

How have you approached optimizing sensor control parameters and what metrics did you use to validate fusion algorithm performance?
Tell us about your experience with hardware-in-the-loop testing and computer-in-the-loop simulation for autonomous systems.
Describe your experience working with DoD or defense customers—how did you handle requirement clarification and field support?
How do you approach learning and integrating new algorithms or software tools with minimal supervision in a fast-paced environment?
What experience do you have with containerization, Linux deployment, and processing large sensor datasets at scale?

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