Senior/Principal RAN Digital Twin & AI Simulation Engineer

Responsibilities

• Own the technical architecture and roadmap for a modular, multi-RAT RAN digital twin
• covering LTE, 5G NR.
• 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 behaviour, 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 behaviour, 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.

Requirements

• BSc or MSc in Electrical Engineering, Computer Engineering, Computer Science, or a related field,
• with substantial relevant industry experience.
• Typically, 7+ years of hands-on experience in communication systems, system development, integration,
• or simulation. Experience with RAN, modem/PHY, or wireless systems is an advantage.
• 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.
• 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

Preferred

• A PhD in wireless communications, signal processing or a related area.
• 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.
• 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.
• Knowledge of digital communications and signal processing, including areas such as channel estimation, equalization, coding/modulation, MIMO, propagation and fading models.
• Extend the current LTE simulation capability and define reusable abstractions that support additional
• 5G NR and 2G stacks without duplicating the platform.
• 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.

What you bring

clearly across research, product, development, and validation teams.