Wireless and Edge AI/ML Engineer

Booz Allen Hamilton•Annapolis Junction, MD

8d•$99,000 - $225,000

About The Position

Booz Allen is seeking an AI/ML Engineer to help build a converged platform that performs real-time wireless signal processing and AI inference on the same GPU-accelerated hardware. The engineer will develop, optimize, and deploy models that operate alongside live network workloads, subject to microsecond latency budgets and shared compute resources. The work covers the full machine-learning lifecycle, from network-telemetry data pipelines and model training to GPU-optimized inference and containerized Kubernetes deployment in a hands-on lab. The role includes developing wireless-network AI applications, integrating them with standardized interfaces, instrumenting the platform with MLOps tooling, and benchmarking resource contention between signal processing and inference workloads. The engineer will also apply security and assurance practices to the ML lifecycle, maintain technical documentation alongside lab work, collaborate with network, RF, and cybersecurity engineers, and brief results to government stakeholders. Applicants must be able to obtain a Secret clearance.

Requirements

At least 3 years of experience training and evaluating ML models with frameworks such as PyTorch or TensorFlow, including exporting models to deployment formats such as ONNX.
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 methods such as quantization or pruning.
Experience with GPU computing, including CUDA, cuDNN, or GPU-accelerated libraries, and NVIDIA driver and container tooling in a Linux server environment.
Experience deploying ML models as services in containerized environments such as Docker or Kubernetes.
Experience building data pipelines for time-series or telemetry data, including feature engineering and dataset versioning.
Experience delivering work 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, including NVIDIA Aerial, GPU-accelerated Layer 1, or AI/ML-based RAN optimization.
Experience with O-RAN architectures, including Near-RT and Non-RT RIC, xApp/rApp development, and the E2 and A1 interfaces.
Experience using 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 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 such as 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 DoD STIG or RMF requirements for ML systems.

Responsibilities

Design, train, and evaluate ML models for wireless-network use cases such as 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 through quantization, pruning, TensorRT or ONNX Runtime conversion, CUDA profiling, and latency and throughput benchmarking.
Package and deploy models as containerized inference services on Kubernetes using NVIDIA Triton Inference Server, 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; validate them end to end in the lab.
Instrument the platform with MLOps tooling for 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 document the operating envelope for co-location on a single platform.
Apply ML lifecycle security and assurance practices, including model provenance, dataset lineage, adversarial robustness testing, and alignment with DoD AI assurance guidance.
Write and maintain 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 for the deployed system and brief results to government stakeholders.