Machine Learning Research Engineer

Booz Allen Hamilton•Springfield, VA

1d•$99,000 - $225,000

About The Position

Machine Learning Research 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 Machine Learning (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 the creation of physics-aware foundational models for remote sensing applications. As a machine learning engineer on our national security team, you’ll train, test, deploy, and maintain models that learn from data. In this role, you’ll own and define the direction of mission-critical solutions by applying best-fit ML algorithms and technologies. You’ll be part of a large community of machine learning engineers across the company and collaborate with data engineers, data scientists, solutions architects, and remote sensing scientists to deliver world class solutions to turn a detailed technical design into a stable, high-performing, well-evaluated PyTorch system. You will work across self-supervised pretraining, lab-to-scene alignment, multi-task model training, uncertainty calibration, benchmarking, and release readiness. This role is ideal for someone who can bridge model research and production-grade ML engineering. 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 applied remote sensing. Join us. The world can’t wait.

Requirements

4+ years of experience with ML engineering, research engineering, or applied ML development
Experience with PyTorch, including building and training deep learning models
Experience with transformer-based models, self-supervised learning, multi-task learning, or large-scale training pipelines
Experience with debugging model training issues such as instability, memory bottlenecks, dataloader performance, and reproducibility
Experience with software engineering fundamentals, including testing, code review, and maintainable ML workflows
Active TS/SCI clearance; willingness to take a polygraph exam
Bachelor’s degree in Computer Science, Machine Learning, Applied Mathematics, Physics, or Remote Sensing

Nice To Haves

Experience with computer vision, scientific imaging, remote sensing, or hyperspectral data
Experience with masked autoencoders, contrastive learning, retrieval models, or multimodal alignment
Experience with uncertainty estimation, calibration, conformal prediction, or OOD detection
Experience with distributed training, mixed precision, and GPU performance optimization
Experience supporting model evaluation and qualification in high-stakes or research-heavy domains
Master’s degree in Computer Science, Machine Learning, Applied Mathematics, Physics, Remote Sensing, or a related field preferred
Doctorate degree in Computer Science, Machine Learning, Applied Mathematics, Physics, Remote Sensing, or a related field a plus