Senior​/Principal RAN Digital Twin & AI Simulation Engineer

Wireless Systems Engineer

Parallel Wireless is a U.S.

-based pioneer in Open RAN innovation, transforming how mobile networks are built, optimized, and powered. Through our GreenRAN™ portfolio, we help operators deliver secure, energy-efficient, automated, and flexible connectivity across 2G, 3G, 4G, 5G, and the path toward 6G. Our software-centric, hardware-agnostic approach brings intelligence into the RAN while helping customers reduce complexity and total cost of ownership.

Parallel Wireless is 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 , 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.

What you will do:

• Own the technical architecture and roadmap for a modular, multi-RAT RAN digital twin covering LTE, 5G , and, where required, 2G/GSM.
• Integrate production MAC and scheduler software into deterministic, per-TTI/slot closed-loop simulations through stable and maintainable interfaces.
• Model the interaction among scheduler decisions, PHY processing, propagation channels, UE behavior, traffic, interference, mobility, HARQ, link adaptation, and power control.
• Extend the current LTE simulation capability and define reusable abstractions that support additional 5G  2G stacks without duplicating the platform.
• Design a fidelity ladder that combines high-fidelity PHY execution with faster calibrated models or lookup/surrogate backends, selecting the least expensive model that is valid for each engineering question.
• Develop and evaluate AI/ML-based RAN capabilities, including neural channel estimation, learned link adaptation or scheduling policies, and ML-based PHY or channel surrogates.
• Build representative datasets and experiment pipelines; establish conventional algorithmic baselines; measure accuracy, robustness, generalization, latency, and compute cost before recommending integration into production software.
• Create reproducible A/B experiments across software builds and algorithm versions, using defined scenarios, seeds, configurations, and KPIs such as throughput, BLER/ACK-NACK behavior, MCS, resource-block allocation, SINR, transmit power, latency, and fairness.
• Establish simulation verification and validation practices: matched sim-vs-lab scenarios, calibration rules, lab-repeatability baselines, divergence analysis, model-version tracking, and evidence reports.
• Prevent overfitting the twin to a single setup by separating universal model parameters, setup-specific calibration, and the production algorithms under test.
• Build automated unit, component, end-to-end, regression, and performance tests and integrate them into CI/CD workflows.
• Improve simulation speed, scale, observability, and usability so that stack, PHY, test, and AI engineers can run repeatable experiments independently.
• Debug discrepancies across C/C++, Python, MATLAB, PHY models, production stack behavior, configuration, and reference measurements.
• Document model…