Machine Learning Research Engineer

Booz Allen Hamilton

Quick summary

Work type: On-site
Location: Springfield, VA
Salary: $99,000–$225,000 / yr
Posted: 2 days ago
Closes: Sep 1, 2026
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $204k

This role $162k

$81k most similar roles pay here $264k

This role pays less than 74% of similar roles. Most pay $162,000–$246,150 — the shaded band above. At the midpoint, this role pays about $162k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Booz Allen Hamilton

Booz Allen Hamilton is a management and technology consulting firm that provides analytics, digital, engineering, and cybersecurity solutions primarily to U.S. government agencies and commercial clients. Industry: Management & Technology Consulting

Booz Allen Hamilton currently has 670 open roles on FindRole.

Listed pay typically runs $86,800–$198,000 across 657 roles with salary data.

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View all roles at Booz Allen Hamilton

At a glance

TL;DR · Machine Learning Research Engineer

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As an experienced Machine Learning Research Engineer on our national security team, you will collaborate with data engineers, scientists, and remote sensing experts to develop physics-aware foundational models for remote sensing applications using PyTorch. Your daily tasks include training, testing, deploying, and maintaining ML models that learn from large datasets, focusing on self-supervised pretraining, lab-to-scene alignment, multi-task model training, uncertainty calibration, benchmarking, and release readiness. Ideal candidates have 4+ years of experience in ML engineering with proficiency in PyTorch, transformer-based models, and software engineering fundamentals such as testing and code review. Experience with computer vision, scientific imaging, remote sensing, and distributed training is a plus, along with TS/SCI clearance for high-stakes environments.

Skills

PyTorch Transformer-based models Self-supervised learning Multi-task learning Docker CI/CD Python PostgreSQL Git GitHub Jupyter Notebook TensorFlow Kubernetes AWS Google Cloud Platform Azure Machine Learning Hyperspectral data Uncertainty estimation Conformal prediction OOD detection Masked autoencoders Contrastive learning Retrieval models Multimodal alignment

What you'll do

Train and test machine learning models using PyTorch for remote sensing applications.
Develop physics-aware foundational models to enhance data processing accuracy.
Collaborate on multi-task model training, uncertainty calibration, and benchmarking.
Debug complex issues in deep learning model training and deployment pipelines.
Optimize GPU performance and implement distributed training strategies for scalability.
Ensure model evaluation and qualification meet high standards in national security contexts.

What we're looking for

4+ years of experience in ML engineering, research, or applied development.
Proficient in PyTorch for building and training deep learning models.
Expertise in transformer-based models, self-supervised learning, and multi-task learning.
Strong debugging skills for model training issues like instability and memory bottlenecks.
Solid background in software engineering practices including testing and code review.
Active Top Secret clearance required.

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