PHY Algorithms Technical Lead

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.

• 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)

Parallel Wireless is expanding the ecosystem for Open RAN with the GreenRAN™ energy-efficient Hardware-Agnostic technology. Deployed worldwide, our comprehensive 2G/3G/4G/5G Macro RAN solutions enhance network security while reducing operating expenses. As pioneers of Open RAN, we prioritize innovation, flexibility, and sustainability to help build a more connected, and green networks. Headquartered in the USA with global R&D centers, we are proud to serve over 60 customers worldwide and have been recognized with over 100 industry awards. Our mission is to accelerate GSMA’s Mobile Net Zero initiative by reducing TCO and driving innovation across the telecom ecosystem.Learn more at www.parallelwireless.com.

Parallel Wireless embraces diversity and equality of opportunity. We are committed to building inclusive and diverse teams representing all backgrounds, with a wide range of perspectives, and empowering industry-leading skills. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.Parallel Wireless does not accept unsolicited resumes or applications from agencies or individuals. Please do not forward resumes to our jobs alias, Parallel Wireless employees, or any other company location. Parallel Wireless is not responsible for any fees related to unsolicited resumes/applications.