Experience with RF theory, digital signal processing, or physical-layer algorithms, such as channel estimation, beamforming, and modulation classification, including GNU Radio or MATLAB
Experience with AI-RAN or AI-native wireless platforms: NVIDIA Aerial, GPU-accelerated Layer 1, or AI/ML-based RAN optimization
Experience with O-RAN architectures, including the RAN Intelligent Controller Near-RT and Non-RT RIC, xApp/rApp development, and the E2 and A1 interfaces
Experience with 5G/LTE network KPIs, performance counters, and telemetry, such as 3GPP performance management data, as ML training inputs
Experience deploying and optimizing large language models (LLMs) for inference using frameworks such as vLLM or NVIDIA TensorRT-LLM, including GPU memory optimization, latency, and throughput benchmarking
Experience with a range of neural network architectures, including transformers, graph neural networks, and reinforcement learning for control and scheduling problems
Experience with C/C++ or other systems programming languages for performance-critical applications or GPU-accelerated computing
Experience with MLOps platforms and tooling, including MLflow, Kubeflow, DVC, or equivalent
Experience developing automated tests for ML data pipelines, model validation, and performance regression testing
Experience with DoD AI assurance and responsible AI guidance and with DoD STIG or RMF requirements for ML systems
