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Parallel Wireless

Wireless Systems Software Engineer

Kfar Saba, Israel

Location
Kfar Saba, Israel
Work
Hybrid
Employment
Full-time
Level
Not stated
Function
Engineering
Pay
Not stated
Posted
7 weeks ago

About the 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.

Parallel Wireless is looking for a hands-on systems engineer to join the development of a multi-RAT digital twin for our Open RAN solution. The role will involve contributing to the integration of production RAN software with PHY, channel, UE, traffic, and network models, and supporting the development of simulation capabilities for LTE, 5G NR, and 2G. This is a great opportunity to gain hands-on experience in RAN systems, simulation, and AI/ML within a highly technical environment.

What will you do

  • Contribute to the development of a modular, multi-RAT RAN digital twin covering LTE and 5G NR.
  • Develop and integrate production software components with simulation and analysis tooling using stable and maintainable interfaces.
  • Work with software and system teams to model communication-system behavior, including traffic, mobility, scheduling, interference, and performance KPIs.
  • Support the extension of existing LTE simulation capabilities and reusable components for additional radio technologies.
  • Develop software components and tools in C/C++ and Python for simulation, automation, data processing, and analysis.
  • Support the development and evaluation of AI/ML-based capabilities for communication systems and network optimization.
  • Build datasets, experiment pipelines, and automated analysis tools to evaluate system behavior and algorithm performance.
  • Create reproducible tests and experiments across software builds and algorithm versions using defined scenarios, configurations, and KPIs.
  • Support simulation verification and validation activities, including comparison with lab results and analysis of model behavior.
  • Build automated unit, component, end-to-end, regression, and performance tests and integrate them into CI/CD workflows.
  • Improve simulation speed, usability, observability, and automation so engineering teams can run repeatable experiments independently.
  • Debug issues across C/C++, Python, MATLAB, system software, configuration, and reference measurements.
  • Document assumptions, supported scenarios, limitations, and experiment results in a clear and structured way.
  • Work closely with RAN, PHY, system, AI/ML, automation, and validation teams to turn engineering questions into measurable experiments.

What you bring

  • B.Sc. in Electrical Engineering or an M.Sc. in a relevant field.
  • At least 2 years of hands-on software development experience in communication systems or a related telecom environment.
  • Strong software development skills in C or C++ and Python.
  • Good understanding of communication systems and the ability to learn new technologies and system concepts quickly.
  • Experience working in Linux development environments with Git, automated testing, and modern software development practices.
  • Strong analytical and problem-solving skills, with the ability to investigate complex technical issues independently.
  • Good communication skills and the ability to work effectively with software, system, algorithms, and validation teams.
  • Simulation experience is not required; candidates with strong software and communication-system backgrounds are encouraged to apply.

Nice to have

  • Experience with simulation environments, system-level modelling, or hardware-in-the-loop testing.
  • Knowledge of LTE and/or 5G NR, RAN, modem/PHY, or wireless communication systems.
  • Experience with MATLAB and communication-system simulation or analysis tools.
  • Hands-on experience with scientific computing and data analysis using NumPy, SciPy, pandas, or similar tools.
  • Exposure to AI/ML, time-series analysis, signal processing, or network optimization.
  • Familiarity with PyTorch, TensorFlow, Hugging Face, or other machine-learning frameworks.
  • Knowledge of digital communications and signal processing concepts.
  • Experience with automated testing, containers, CI/CD, or cloud development environments.
  • Familiarity with SDR, radio test equipment, lab automation, or field-log analysis.

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.

Source

Posted by Parallel Wireless on their own hiring page, jobs.lever.co. OptiTalent shows it as published, read 07 Nov 2026 00:43 UTC; the original is the reference.

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