Senior/Principal RAN Digital Twin, AI Simulation Engineer

🔥 14 hours ago

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🤖 Artificial Intelligence

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👻 Ghost score 11%

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Parallel Wireless

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2012

📡 Telecommunications

🔧 Hardware

Telecommunications • Hardware

Parallel Wireless is a technology company that pioneers innovative wireless infrastructure solutions through its OpenRAN technology. The company focuses on providing cost-effective and flexible wireless networks covering all generations from 2G to 5G. Parallel Wireless aims to facilitate mobile operators by delivering coverage in urban, suburban, and rural areas while enhancing capacity and performance using cloud-native and software-driven approaches. Through their OpenRAN technology, they are able to deploy macro, massive MIMO, and small cell networks, offering advanced 5G capabilities and supporting easy technology migration and upgrades. Known for reducing complexities and operational costs, Parallel Wireless enables dynamic and automated network management and analytics. Their mission involves driving modern wireless solutions that ensure widespread and equal access to advanced connectivity worldwide.

📋 Description

• Own the technical architecture and roadmap for a modular, multi-RAT RAN digital twin covering LTE, 5G NR, and 2G • Integrate production MAC and scheduler software into deterministic, per-TTI/slot closed-loop simulations • Model interactions among scheduler decisions, PHY processing, propagation channels, UE behavior, traffic, interference, mobility, HARQ, link adaptation, and power control • Extend LTE simulation capability and define reusable abstractions for 5G NR and 2G stacks • Design a fidelity ladder combining high-fidelity PHY execution with calibrated models and surrogate backends • 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 datasets and experiment pipelines; establish baselines; measure accuracy, robustness, generalization, latency, and compute cost • Create reproducible A/B experiments using defined scenarios, seeds, configurations, and KPIs • Establish simulation verification and validation practices, calibration rules, repeatability baselines, divergence analysis, model-version tracking, and evidence reports • Separate universal model parameters, setup-specific calibration, and production algorithms under test • Build automated unit, component, end-to-end, regression, and performance tests integrated into CI/CD • Improve simulation speed, scale, observability, and usability • Debug discrepancies across C/C++, Python, MATLAB, PHY models, production stack behavior, configuration, and reference measurements • Document model assumptions, limitations, operating regions, calibration provenance, and backend validity • Collaborate with RAN stack, PHY, system architecture, AI/ML, automation, and lab-validation teams

🎯 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 • Experience building or validating link-level, system-level, or hardware-in-the-loop simulations • Strong programming skills in C or C++ and Python • Experience with scientific computing and data analysis using NumPy, SciPy, pandas, and visualization frameworks • Experience with reproducibility, baselines, error analysis, uncertainty, calibration, controlled comparisons, and avoidance of data leakage or curve fitting • Experience with Linux, Git, automated testing, containers, and CI/CD • PhD in wireless communications, signal processing, or related area is nice to have • Experience with machine-learning models for communications, signal processing, time-series data, or related domains using PyTorch, TensorFlow, or equivalent is nice to have • 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 nice to have • Knowledge of digital communications and signal processing is nice to have • Experience with MATLAB and Communications/LTE/5G toolboxes or equivalent is nice to have • Experience with production eNodeB/gNodeB software, commercial modem stacks, or Open RAN products is nice to have • Knowledge of 3GPP LTE, NR, and/or GERAN specifications is nice to have • Experience with scheduler algorithms is nice to have