PHY Algorithms Senior Engineer - AI/ML
Kfar Saba • Full Time
Posted 1mo ago
Job Location
Kfar Saba
Tech Stack
Remote Work Policy
On-site
Employment Type
Full Time
Categories
Machine Learning Engineer
About the job
Parallel Wireless is seeking experienced wireless algorithm experts to research and design advanced cellular communication algorithms for 5G and beyond products. This role will leverage neural networks and machine learning techniques to reimagine mobile networks with innovative, energy-efficient Open RAN solutions. The ideal candidate will contribute to the future of telecommunications by driving innovation through green and sustainable networks.
Responsibilities
- Conduct algorithmic research focusing on ML-based approaches for PHY layer processing, balancing performance, implementation cost, real-time constraints, and time-to-market.
- Design and train neural network models for PHY tasks like channel estimation, signal detection, beamforming, and decoding for real-time inference on embedded platforms.
- Develop algorithms from research and simulation through to customer releases, including literature reviews, ML model prototyping, specification writing, and supporting implementation and integration.
- Evaluate and benchmark ML-based solutions against traditional DSP approaches, considering accuracy, latency, computational cost, and system performance.
Requirements
- 3+ years of hands-on experience with deep learning frameworks (PyTorch, TensorFlow, or similar) and neural network architectures (CNNs, RNNs, transformers, autoencoders).
- Familiarity with model optimization techniques for real-time deployment (quantization, pruning, knowledge distillation, hardware-aware neural architecture search).
- Independent problem solver with excellent mathematical and analytical skills.
- Eager to learn and develop professional skills in wireless communications and applied machine learning.
- Team player with excellent communication skills, able to thrive in a global multi-site environment.
- 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) is an advantage.
- Experience in PHY algorithms development for wireless modems is an advantage.
- Good understanding of cellular standards (LTE/NR) is an advantage.
- Experience with ONNX Runtime, TensorRT, or similar inference engines is an advantage.
- M.Sc / PhD in electrical engineering (major in communication theory and systems, signal processing, and/or machine learning) is an advantage.