> Markdown version of [/jobs/ext/1029716-machine-learning-research-engineer](https://www.wearedevelopers.com/jobs/ext/1029716-machine-learning-research-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Research Engineer - **Company:** Booz Allen Hamilton Inc. - **Location:** Springfield, United States - **Experience:** Experienced - **Salary:** $99,000.0 - $225,000.0 - **Contract:** Permanent contract - **Skills:** Computer Vision, Code Review, Software Debugging, Distributed Computing Environment, Machine Learning, Object-Oriented Software Development, Performance Tuning, Pytorch, Deep Learning, Model Validation, Information Technology, Machine Learning Operations, GPT - **Published:** June 2, 2026 - **Apply:** https://www.dice.com/job-detail/3d04216b-1984-40f4-960b-c26fd2dc401f ## About the Role 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., * 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 * Top Secret clearance * Bachelor's degree Nice If You Have: * 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 * TS/SCI clearance * 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 ## Related Videos - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Are Code Reviews Worth It? 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