Senior/Principal RAN Digital Twin & AI Simulation EngineerAI

What you will do: • Own the technical architecture and roadmap for a modular, multi-RAT RAN digital twin covering LTE, 5G NR, 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 NR and 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 assumptions, limitations, supported operating regions, calibration provenance, and the validity of every simulation or ML backend. • Work closely with RAN stack, PHY, system architecture, AI/ML, automation, and lab-validation teams to convert product questions into measurable simulation campaigns. What you bring: • 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. Nice to have: • Experience with MATLAB and Communications/LTE/5G toolboxes or equivalent PHY simulation environments. • Direct experience with production eNodeB/gNodeB software, commercial modem stacks, or Open RAN products. • Knowledge of 3GPP LTE, NR, and/or GERAN specifications and experience translating standards into executable models and test scenarios. • Experience with scheduler algorithms such as proportional fair, round robin, maximum C/I, QoS-aware scheduling, or reinforcement-learning-based resource allocation. • Experience with neural channel estimation, learned receivers, differentiable communications, model compression, or ML inference in latency-constrained systems. • Experience with multi-cell interference, mobility, carrier aggregation, massive MIMO, beam management, or realistic traffic and UE-population modeling. • Experience with GPU acceleration, CUDA, distributed simulation, batch experiment orchestration, or cloud/HPC execution. • Familiarity with SDR, radio test equipment, lab automation, field-log analysis, or simulation-to-lab correlation. • Experience building internal engineering platforms, APIs, dashboards, and self-service experiment workflows.