PHY Algorithms Technical Lead

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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.

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.

M.Sc / PhD in electrical engineering (major in communication theory and systems, signal processing, and/or machine learning - Advantage).