Job description

Wireless and Edge AI/ML Engineer The Opportunity: At Booz Allen, we power possibility for our clients facing their toughest challenges. Our cross-domain team of consultants, technologists, researchers, engineers, and cyber experts builds innovative solutions for the infrastructure and capabilities of tomorrow. We re trusted partners on our clients most critical missions, and we bring expertise to the table, but it s our attitude, our courage, curiosity, and passion for tomorrow, that makes us unique. We re seeking an AI/ML Engineer to join a team building a converged platform that runs real-time wireless signal processing and AI inference on the same GPU-accelerated hardware. You will develop, optimize, and deploy models that run alongside live network workloads, where latency budgets are measured in microseconds and compute is shared with the radio stack. The work spans the full lifecycle: data pipelines from network telemetry, model training and benchmarking, GPU-optimized inference, and containerized deployment on Kubernetes in a hands-on lab. What You Will Do: 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 RF data captured in the lab Build data pipelines that ingest, label, and version telemetry from the network stack, such as 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: quantization, pruning, TensorRT or ONNX Runtime conversion, CUDA profiling, and latency and throughput benchmarking 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: 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: 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, concurrently with lab work, not after the fact 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 Join us. The world can t wait. You Have: 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 3 years of experience with software development in Python, including production-quality code, testing, and version control practices Experience optimizing and benchmarking trained neural networks for inference: 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 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, or 8 years of experience in AI/ML or Automation using Python, Ansible, Terraform, and Kubernetes in lieu of a degree Nice If You Have: 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 DISA STIG or RMF requirements for ML systems Clearance: Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information. Compensation At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen’s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page. Salary at Booz Allen is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $99,000.00 to $225,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen’s total compensation package for employees. This posting will close within 90 days from the Posting Date. Identity Statement As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud. Candidate AI Usage Policy AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided . Work Model Our people-first culture prioritizes the benefits of collaboration. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings. Remote : If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility. Hybrid : If this position is listed as hybrid, you will be expected to work from a Booz Allen or customer facility, in alignment with your leadership s expectations and the needs of the role. Onsite : If this position is listed as onsite, work will primarily be performed full-time at a customer facility, where employees will collaborate directly with colleagues and customers as required by the role. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.

Requirements

Must-haves

Wireless and Edge AI/ML Engineer The Opportunity:
Build data pipelines that ingest, label, and version telemetry from the network stack, such as 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: quantization, pruning, TensorRT or ONNX Runtime conversion, CUDA profiling, and latency and throughput benchmarking 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: 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: 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, concurrently with lab work, not after the fact
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
Join us. The world can t wait.
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
3 years of experience with software development in Python, including production-quality code, testing, and version control practices
Experience optimizing and benchmarking trained neural networks for inference: 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
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

Nice-to-haves

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Keywords to mirror

aimlkubernetesci/cdpythondockeragilellm

Platform: workday
Effort: long

Benefits

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Professional Development