Hybrid PHY Algorithms Technical Lead

at Parallel Wireless

• Job title: PHY Algorithms Technical Lead
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Postgraduate Degree
• Tech skills: Python, PyTorch, Tensorflow
• Location requirements: Hybrid, Kfar Saba, Israel

About the role

• Research and design advanced cellular communication algorithms leveraging ML techniques for 5G products at Parallel Wireless. Join a team reimagining mobile networks with innovative solutions.

Responsibilities

• Algorithmic research considering the trade-offs between performance, implementation cost, real-time constraints, and time-to-market - with emphasis on ML-based approaches for PHY layer processing.
• Design and train neural network models for PHY tasks such as channel estimation, signal detection, beamforming, and decoding, targeting real-time inference on embedded platforms.
• Algorithms development from research to simulation level to official customer releases, including literature survey, ML model prototyping (Python/PyTorch/TensorFlow), Matlab modeling, specification documents, escorting implementation & end-to-end integration process.
• Evaluate and benchmark ML-based solutions against traditional DSP approaches in terms of accuracy, latency, and computational cost.

Requirements

• 3+ years of hands-on experience with deep learning frameworks (PyTorch, TensorFlow, or similar) and neural network architectures (CNNs, RNNs, transformers, autoencoders).
• Experience applying ML/DL to physical layer problems (e.g., channel estimation, MIMO detection, CSI feedback, learned codebooks, or end-to-end learned communication systems) - Advantage.
• Experience in PHY algorithms development for wireless modems - Advantage.
• Familiarity with model optimization techniques for real-time deployment: quantization, pruning, knowledge distillation, and hardware-aware neural architecture search.
• An independent problem solver with excellent mathematical and analytical skills.
• Eager to learn and develop your professional skills in the fields of wireless communications and applied machine learning.
• Team player: Excellent communication skills, and ability to thrive in a global multi-site environment.
• Good understanding of the cellular standards (LTE/NR) - Advantage.
• Experience with ONNX Runtime, TensorRT, or similar inference engines - Advantage.