Booz Allen
MachineLearningResearchEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Machine Learning Research Engineer at Booz Allen. Skills: Machine Learning, Research Engineering, MLOps, Remote Sensing. Train models. Test models”
Industry & Context.
Problem-solve
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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