we are looking for a hands-on wireless systems engineer to lead the development of a multi-RAT digital twin for our Open RAN solution. The digital twin will execute production RAN software-beginning with scheduler and MAC behavior-in a closed loop with PHY, channel, UE, traffic, and network models. It will allow engineering teams to design, evaluate, and compare features for LTE, 5G NR, and 2G without requiring a dedicated physical radio setup for every development cycle.
This is a senior individual-contributor role at the intersection of wireless systems, simulation, production software, and AI/ML. You will evolve an existing LTE end-to-end simulator into a scalable engineering platform for feature development, regression testing, performance optimization, and evidence-based pre-validation. Initial use cases include MAC scheduler and link-adaptation improvements, power control, mobility and interference scenarios, and neural-network-assisted channel estimation.
The successful candidate will understand that a useful digital twin must be both fast and trustworthy. You will define multiple fidelity levels-from rapid surrogate models to full PHY processing-and establish repeatable methods for calibrating the twin against lab or field reference data. The goal is to reduce dependence on continuous lab access while maintaining clear, measurable confidence in the simulation results.
This is a senior individual-contributor role at the intersection of wireless systems, simulation, production software, and AI/ML. You will evolve an existing LTE end-to-end simulator into a scalable engineering platform for feature development, regression testing, performance optimization, and evidence-based pre-validation. Initial use cases include MAC scheduler and link-adaptation improvements, power control, mobility and interference scenarios, and neural-network-assisted channel estimation.
The successful candidate will understand that a useful digital twin must be both fast and trustworthy. You will define multiple fidelity levels-from rapid surrogate models to full PHY processing-and establish repeatable methods for calibrating the twin against lab or field reference data. The goal is to reduce dependence on continuous lab access while maintaining clear, measurable confidence in the simulation results.
Requirements:
BSc or MSc in Electrical Engineering, Computer Engineering, Computer Science, or a related field,
with substantial relevant industry experience. A PhD in wireless communications, signal processing, or a related area
is an advantage.
Typically 7+ years of hands-on experience in wireless systems, RAN development, modem/PHY development,
or system/link-level simulation; exceptional candidates with equivalent depth are welcome.
Deep knowledge of LTE and/or 5G NR L1/L2 behavior, including MAC scheduling, link adaptation,
HARQ, CQI/SINR feedback, resource allocation, and uplink power control.
Strong understanding of digital communications and signal processing, including channel estimation,
equalization, coding/modulation, MIMO, propagation and fading models, and performance metrics.
Demonstrated experience building or validating link-level, system-level, or hardware-in-the-loop simulations and
explaining where a model is-and is not-valid.
Strong programming skills in C or C++ and Python, including the ability to integrate production native code with
simulation and analysis tooling.
Practical experience with scientific computing and data analysis using tools such as NumPy, SciPy, pandas,
and visualization frameworks.
Hands-on experience developing or evaluating machine-learning models for communications, signal processing,
time-series data, or related domains using PyTorch, TensorFlow, or an equivalent framework.
Sound experimental and statistical judgment: reproducibility, baselines, error analysis, uncertainty, calibration,
controlled comparisons, and avoidance of data leakage or curve fitting.
Experience working in Linux development environments with Git, automated testing, containers, and CI/CD.
Ability to lead a technically ambiguous initiative, make architecture decisions, and communicate clearly
across research, product, development, and validation teams.
BSc or MSc in Electrical Engineering, Computer Engineering, Computer Science, or a related field,
with substantial relevant industry experience. A PhD in wireless communications, signal processing, or a related area
is an advantage.
Typically 7+ years of hands-on experience in wireless systems, RAN development, modem/PHY development,
or system/link-level simulation; exceptional candidates with equivalent depth are welcome.
Deep knowledge of LTE and/or 5G NR L1/L2 behavior, including MAC scheduling, link adaptation,
HARQ, CQI/SINR feedback, resource allocation, and uplink power control.
Strong understanding of digital communications and signal processing, including channel estimation,
equalization, coding/modulation, MIMO, propagation and fading models, and performance metrics.
Demonstrated experience building or validating link-level, system-level, or hardware-in-the-loop simulations and
explaining where a model is-and is not-valid.
Strong programming skills in C or C++ and Python, including the ability to integrate production native code with
simulation and analysis tooling.
Practical experience with scientific computing and data analysis using tools such as NumPy, SciPy, pandas,
and visualization frameworks.
Hands-on experience developing or evaluating machine-learning models for communications, signal processing,
time-series data, or related domains using PyTorch, TensorFlow, or an equivalent framework.
Sound experimental and statistical judgment: reproducibility, baselines, error analysis, uncertainty, calibration,
controlled comparisons, and avoidance of data leakage or curve fitting.
Experience working in Linux development environments with Git, automated testing, containers, and CI/CD.
Ability to lead a technically ambiguous initiative, make architecture decisions, and communicate clearly
across research, product, development, and validation teams.
This position is open to all candidates.
