Wireless and Edge AI/ML Engineer

Booz Allen Hamilton • Annapolis Junction, MD

1d • $99,000 - $225,000

About The Position

Booz Allen is seeking an AI/ML Engineer to help build a converged platform that runs real-time wireless signal processing and AI inference on the same GPU-accelerated hardware. The role involves developing, optimizing, and deploying models alongside live network workloads, with microsecond latency budgets and compute shared with the radio stack. The work covers the full lifecycle, from network-telemetry data pipelines and model training to GPU-optimized inference and containerized deployment on Kubernetes in a hands-on lab.

The engineer will collaborate with network, RF, and cybersecurity engineers to translate mission requirements into deployed models, validate applications in the lab, and brief results to government stakeholders. The position also requires maintaining technical documentation throughout lab work and applying security and assurance practices to the ML lifecycle.

Requirements

• At least 3 years of experience training and evaluating ML models using frameworks such as PyTorch or TensorFlow, including exporting models to formats such as ONNX for deployment.
• At least 3 years of Python software-development experience, including production-quality code, testing, and version-control practices.
• Experience optimizing and benchmarking trained neural networks for inference, including latency and throughput measurement, profiling, and techniques such as quantization or pruning.
• Experience with GPU computing, including CUDA, cuDNN, or GPU-accelerated libraries, and with NVIDIA driver and container tooling in a Linux server environment.
• Experience deploying ML models as services in containerized environments such as Docker or Kubernetes, and building data pipelines for time-series or telemetry data, including feature engineering and dataset versioning.
• Experience delivering in an Agile, cross-functional engineering team and resolving complex problems with limited direction.
• Ability to work hands-on in a fast-paced lab environment with hard delivery deadlines.
• Ability to produce clear, accurate technical documentation and brief results to technical and non-technical audiences.
• Ability to obtain a Secret clearance.
• One of the following education and experience combinations: a bachelor's degree in a STEM field and at least 5 years of experience in AI/ML or automation using Python, Ansible, Terraform, and Kubernetes; a master's degree in a STEM field and at least 3 years of such experience; or at least 8 years of such experience in lieu of a degree.

Nice To Haves

• 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, such as NVIDIA Aerial, GPU-accelerated Layer 1, or AI/ML-based RAN optimization.
• Experience with O-RAN architectures, including the Near-RT and Non-RT RAN Intelligent Controller, 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 and latency and throughput benchmarking.
• Experience with 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 DISA STIG or RMF requirements for ML systems.

Responsibilities

• Design, train, and evaluate ML models for wireless network use cases, including traffic prediction, anomaly detection, interference classification, resource scheduling, and energy optimization, using network telemetry, KPIs, and lab-captured RF data.
• Build data pipelines to ingest, label, and version network-stack telemetry—including performance counters, logs, packet captures, and I/Q samples—into reproducible training datasets.
• Optimize trained models for real-time inference on GPU-accelerated edge servers using techniques and tools such as quantization, pruning, TensorRT or ONNX Runtime conversion, and CUDA profiling; benchmark latency and throughput against hard targets.
• Package and deploy models as containerized inference services on Kubernetes using NVIDIA Triton Inference Server, the NVIDIA GPU Operator, and MIG or time-sliced GPU sharing alongside live network workloads.
• Develop AI-driven network applications such as xApps and rApps that consume network telemetry and return control decisions through standardized interfaces, and validate them end to end in the lab.
• Instrument the platform with MLOps tooling, including experiment tracking, model registry, automated retraining triggers, drift monitoring, and CI/CD for model deployment.
• Benchmark GPU resource contention between signal-processing and AI-inference workloads, and characterize and document the operating envelope for co-location on a single platform.
• Apply security and assurance practices to the ML lifecycle, including model provenance, dataset lineage, adversarial robustness testing, and alignment with DoD AI assurance guidance.
• Write and maintain technical documentation, including model cards, training and deployment procedures, benchmark reports, and reproducibility guides, alongside lab work.
• Collaborate with network, RF, and cybersecurity engineers to translate mission requirements into models that run on the deployed system, and brief results to government stakeholders.

Benefits

• Health, life, disability, financial, and retirement benefits.
• Paid leave.
• Professional development.
• Tuition assistance.
• Work-life programs.
• Dependent care.
• Recognition awards for exceptional performance and demonstration of company values.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level