Senior/Principal RAN Digital Twin, AI Simulation Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Take responsibility for the technical architecture and roadmap of a modular, multi-RAT RAN digital twin encompassing LTE, 5G NR, and 2G.

• Integrate production MAC and scheduler software into deterministic, closed-loop simulations per TTI/slot.

• Model the interactions between scheduler decisions, PHY processing, propagation channels, UE behavior, traffic, interference, mobility, HARQ, link adaptation, and power control.

• Enhance LTE simulation capabilities and create reusable abstractions for 5G NR and 2G stacks.

• Design a fidelity ladder that merges high-fidelity PHY execution with calibrated models and surrogate backends.

• Develop and assess AI/ML-driven RAN capabilities, including neural channel estimation, learned link adaptation or scheduling policies, and ML-based PHY or channel surrogates.

• Construct datasets and experiment pipelines; establish baselines; and evaluate accuracy, robustness, generalization, latency, and computational cost.

• Generate reproducible A/B experiments utilizing defined scenarios, seeds, configurations, and KPIs.

• Implement simulation verification and validation practices, calibration rules, repeatability baselines, divergence analysis, model-version tracking, and evidence reports.

• Distinguish universal model parameters, setup-specific calibration, and production algorithms under test.

• Develop automated unit, component, end-to-end, regression, and performance tests integrated within CI/CD frameworks.

• Enhance simulation speed, scalability, observability, and usability.

• Troubleshoot discrepancies across C/C++, Python, MATLAB, PHY models, production stack behavior, configurations, and reference measurements.

• Document model assumptions, limitations, operating regions, calibration provenance, and backend validity.

• Collaborate with teams involved in RAN stack, PHY, system architecture, AI/ML, automation, and lab validation.

⛳️ Requirements

• BSc or MSc in Electrical Engineering, Computer Engineering, Computer Science, or a related field, along with significant relevant industry experience.

• Generally, 7+ years of practical experience in communication systems, system development, integration, or simulation.

• Familiarity with RAN, modem/PHY, or wireless systems is desirable.

• Experience in building or validating link-level, system-level, or hardware-in-the-loop simulations.

• Strong programming capabilities in C or C++ and Python.

• Proficiency in scientific computing and data analysis utilizing NumPy, SciPy, pandas, and visualization frameworks.

• Experience with reproducibility, establishing baselines, error analysis, uncertainty, calibration, controlled comparisons, and avoiding data leakage or curve fitting.

• Proficient in Linux, Git, automated testing, containers, and CI/CD methodologies.

• A PhD in wireless communications, signal processing, or a related field is a plus.

• Experience with machine learning models applicable to communications, signal processing, time-series data, or related fields using PyTorch, TensorFlow, or similar frameworks is advantageous.

• Knowledge of LTE and/or 5G NR L1/L2 behavior, MAC scheduling, link adaptation, HARQ, CQI/SINR feedback, resource allocation, and uplink power control is preferable.

• Familiarity with digital communications and signal processing is beneficial.

• Experience with MATLAB and Communications/LTE/5G toolboxes or their equivalents is a plus.

• Experience with production eNodeB/gNodeB software, commercial modem stacks, or Open RAN products is advantageous.

• Knowledge of 3GPP LTE, NR, and/or GERAN specifications is a plus.

• Experience with scheduler algorithms is beneficial.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Opportunities for professional development and continuous learning.

• Comprehensive health and wellness benefits.

• Flexible work hours and remote work options.

• Collaborative and innovative work environment.