Model design and architecture: Design, train, and optimize deep learning models for detection, segmentation, and classification. Tailor neural networks to process abstract, non-natural 3D, 4D, and 5D radar imagery.

Target classification and tracking: Build advanced neural network architectures to detect and track small objects like birds and drones. Ensure models perform reliably despite real-world background noise and weather.

Data engineering and automation: Prepare, preprocess, and augment large-scale radar datasets for training pipelines. Implement automated labeling strategies to streamline data preparation at scale.

Edge optimization and deployment: Optimize trained neural networks for high-performance inference on edge computing hardware. Collaborate with Software Developers and Radar Specialists to integrate models seamlessly into production systems.

Research and field validation: Evaluate emerging deep learning techniques to continuously lift our radar capabilities. Test model robustness under diverse field conditions and document design choices clearly.

MSc in Data Science, Computer Science, Artificial Intelligence, or a related technical field

2-4 years of experience in Deep Learning, specifically within Computer Vision

Strong proficiency in Python and hands-on experience using PyTorch

Practical experience with edge deployment and model optimization tools like TensorRT or ONNX

Familiarity with non-natural signal modalities such as radar, sonar, LiDAR, or medical imaging

Fluent English, both written and spoken

A proactive mindset and strong problem-solving skills in a collaborative environment

Deep Learning Engineer

Responsibilities

Qualification

Required

Benefits

Company

Robin Radar

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