Remote Embedded Machine Learning Jobs in Virginia

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.

You Have:

• 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 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

• 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

Clearance:

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; TS/SCI clearance is required.

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.

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, whether it occurs in person or virtually. 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 facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.

• Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or 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.