Booz Allen

MachineLearningResearchEngineer

$99–99k Springfield, Virginia, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Machine Learning Research Engineer at Booz Allen. Skills: Machine Learning, Research Engineering, MLOps, Remote Sensing. Train models. Test models”

Industry & Context.

Problems you'll solve

Problem-solve

Eligibility Requirements

Top Secret clearance, TS/SCI clearance

What They're Looking For.

Must Have

4+ years ML engineering, 4+ years research engineering, 4+ years applied ML development, Experience with PyTorch, Experience with transformer-based models, Experience with self-supervised learning, Experience with multi-task learning, Experience with large-scale training pipelines, Experience debugging model training issues, Experience with software engineering fundamentals, Top Secret clearance, Bachelor's degree

Nice to Have

Experience with computer vision, Experience with scientific imaging, Experience with remote sensing, Experience with hyperspectral data, Experience with masked autoencoders, Experience with contrastive learning, Experience with retrieval models, Experience with multimodal alignment, Experience with uncertainty estimation, Experience with calibration, Experience with conformal prediction, Experience with OOD detection, Experience with distributed training, Experience with mixed precision, Experience with GPU performance optimization, Experience supporting model evaluation, Experience supporting model qualification, TS/SCI clearance, Master's degree, Doctorate degree

What You'll Do.

Define direction of solutions

Apply ML technologies

Bridge model research

Bridge production-grade ML engineering

Navigate ML algorithms

Navigate ML frameworks

Solve real-world challenges

Work across self-supervised pretraining

Work across lab-to-scene alignment

Work across multi-task model training

Work across uncertainty calibration

Work across benchmarking

Work across release readiness

Build deep learning models

Train deep learning models

Debug model training issues

Support model evaluation

Support model qualification

How You'll Work.

Team & Collaboration

Collaborate with data engineers; Collaborate with data scientists; Collaborate with solutions architects; Collaborate with remote sensing scientists

Full Job Description

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. **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 instabili

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