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Parallel Wireless

Posted 1 month ago

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PHY Algorithms Senior Engineer - AI/ML

Kfar SabaRemoteFull-time

AI Summary

Senior engineer responsible for research, design, and deployment of ML-based PHY algorithms for 5G/Beyond, including channel estimation, detection, beamforming, and decoding. Works on model prototyping, benchmarking, and integration for real-time embedded platforms.

About this role

Parallel Wireless is reimagining mobile networks with innovative, energy-efficient Open RAN solutions. Join us as we lead the future of telecommunications, driving innovation through green and sustainable networks. Learn more about our mission, vision and values.

We are looking for highly motivated, experienced, and passionate wireless algorithm experts for the research and design of advanced cellular communication algorithms, leveraging neural networks and machine learning techniques, for our 5G and beyond products.

Job 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.

    • Job 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.

    • Education:

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

    • Skills

    AutoencodersChannel EstimationCNNsCNNs For WirelessCSI FeedbackEmbedded PlatformsEnd-to-end Learned Communication SystemsHardware-aware Neural Architecture SearchKnowledge DistillationLearned CodebooksMatlabMIMO DetectionML PrototypingONNX RuntimePruningPythonPyTorchQuantizationReal-time InferenceRNNsTensorFlowTensorRTTransformers

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