Wireless and Edge AI/ML Engineer

What you'll do

• Design, train, and evaluate ML models for wireless network use cases like traffic prediction and anomaly detection.
• Build data pipelines to ingest and version telemetry, logs, and packet captures into reproducible training datasets.
• Optimize models for real-time GPU inference using quantization, pruning, TensorRT, and CUDA profiling.
• Deploy containerized inference services on Kubernetes using NVIDIA Triton Inference Server and GPU sharing technologies.
• Develop AI-driven network applications like xApps and rApps that return control decisions via standardized interfaces.
• Instrument the platform with MLOps tooling for experiment tracking, model registry, and automated retraining.
• Benchmark GPU resource contention between signal processing and AI inference workloads to characterize the operating envelope.
• Apply security practices including model provenance, dataset lineage, and adversarial robustness testing.

What we're looking for

• 3+ years of experience training and evaluating ML models using PyTorch or TensorFlow and exporting to ONNX.
• 3+ years of experience with production-quality Python software development, including testing and version control.
• Experience optimizing and benchmarking neural networks for inference using quantization, pruning, and CUDA profiling.
• Experience with GPU computing, including CUDA, cuDNN, and NVIDIA container tooling in a Linux environment.
• Experience deploying ML models as containerized services using Docker or Kubernetes and building telemetry data pipelines.
• Ability to obtain a Secret clearance.
• Bachelor's degree in a STEM field and 5+ years of experience in AI/ML or Automation using Python, Ansible, Terraform, and Kubernetes.
• Master's degree in a STEM field and 3+ years of experience in AI/ML or Automation using Python, Ansible, Terraform, and Kubernetes.
• 8+ years of experience in AI/ML or Automation using Python, Ansible, Terraform, and Kubernetes in lieu of a degree.
• Experience with RF theory, digital signal processing, or physical-layer algorithms (preferred).
• Experience with AI-RAN or AI-native wireless platforms (preferred).
• Experience with O-RAN architectures, xApp/rApp development, and E2/A1 interfaces (preferred).
• Experience with 5G/LTE network KPIs and 3GPP performance management data (preferred).
• Experience deploying and optimizing large language models (LLMs) for inference (preferred).
• Experience with C/C++ or other systems programming languages (preferred).
• Experience with MLOps platforms such as MLflow, Kubeflow, or DVC (preferred).
• Experience with DoD AI assurance and DISA STIG or RMF requirements (preferred).