Tactical Machine Learning Engineer

Booz Allen Hamilton•Lorton, VA

About The Position

Tactical Machine Learning Engineer The Opportunity: As an experienced engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to conduct statistical analyses on business processes using ML techniques makes you an integral part of delivering a customer-focused solution. We need your technical knowledge and desire to problem-solve to support tactical edge machine learning capabilities. As a machine learning engineer on our tactical edge team, you’ll train, test, deploy, and maintain models that learn from data. In this role, you’ll lead the direction of mission-critical solutions by applying best-fit ML algorithms and introducing leading-edge technologies. You’ll share your knowledge with a large community of machine learning engineers across the company and collaborate with TAK developers, data scientists, and our tactical experts to deliver world-class solutions to the warfighter combining tactical capability with AI and ML capabilities, reducing their workload and increase their effectiveness on mission. Your skills and extensive technical expertise will guide clients as they navigate the landscape of ML algorithms, tools, and frameworks. Work with us to solve real-world challenges and define ML strategy for our warfighters. What You’ll Work On: Develop and maintain Android applications using Kotlin and Java. Integrate ML inference pipelines into production Android apps. Deploy and optimize models to run on NPU, GPU, and CPU. Perform model quantization, including INT8 or FP16, and size or latency optimization. Convert and deploy models from training frameworks to mobile runtimes. Profile and tune performance using Android and vendor toolchains. Ensure efficient, secure, and reliable on-device AI execution. Join us. The world can’t wait.

Requirements

5+ years of experience with Android development
Experience with Kotlin and Java
Experience deploying ML models on Android devices
Experience with TensorFlow Lite, mobile AI SDKs such as Qualcomm QNN, and Android NNAPI or hardware delegates
Experience with model quantization and mobile optimization
Experience with debugging and performance profiling
Knowledge of NPU or GPU acceleration on Android
Secret clearance
Bachelor's degree in a Computer Science field

Nice To Haves

Experience with Python for model training and conversion
Experience with PyTorch, TensorFlow, and ONNX
Experience deploying LLMs, vision, or speech models on mobile
Experience with pruning, distillation, or quantization-aware training
Experience with Samsung device optimization
Experience with ATAK
Experience in edge or network-denied environments

Responsibilities

Develop and maintain Android applications using Kotlin and Java.
Integrate ML inference pipelines into production Android apps.
Deploy and optimize models to run on NPU, GPU, and CPU.
Perform model quantization, including INT8 or FP16, and size or latency optimization.
Convert and deploy models from training frameworks to mobile runtimes.
Profile and tune performance using Android and vendor toolchains.
Ensure efficient, secure, and reliable on-device AI execution.