AI/Machine Learning Research Scientist – Undersea Acoustics, Physics-Based Modeling

Peraton

. Conduct basic and applied research in machine learning, deep learning, generative artificial intelligence, large language models, and physics-based machine learning .

Posted 11/5/2026full-timeStennis Space Center • Mississippi • United StatesMid-LevelSenior💰 $104,000 - $166,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning and deep learning methodologies, particularly in developing and evaluating models for scientific and engineering applications. Proficient in translating complex physics and engineering requirements into effective machine-learning workflows while maintaining rigorous standards of reproducibility and documentation.

Highest-signal resume keywords

Machine Learning ApplicationDeep Learning MethodologiesPyTorch, TensorFlow, or JAX ExperienceUnderwater Acoustics KnowledgePhysics-Informed Neural Networks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningSurrogate ModelingModel EvaluationData ManagementQuantitative ResearchExperimental DesignSignal ProcessingGenerative ModelingNeural Network Optimization

Soft Skills

Analytical SkillsProblem-SolvingTechnical CommunicationCollaborationIndependent Work

Tools & Technologies

PyTorchTensorFlowJAXResearch SoftwareTechnical Documentation

Certifications & Qualifications

Secret Security Clearance

Industry Keywords

Underwater AcousticsAcoustic PropagationWave PhysicsComputational PhysicsDecision-Support Challenges

Tech Stack

Tools & technologies

PyTorchTensorflow

About the role

Key responsibilities & impact

Conduct basic and applied research in machine learning, deep learning, generative artificial intelligence, large language models, and physics-based machine learning
Develop and evaluate advanced computational methods for Navy-relevant scientific, engineering, acoustic, and decision-support challenges
Focus on underwater acoustics and acoustic propagation, with applications in wave physics, signal processing, and related computational-physics domains
Develop rapid machine-learning surrogate models for computationally demanding physics models while preserving decision-relevant fidelity
Collaborate with acousticians, physicists, engineers, materials scientists, computer scientists, and other subject-matter experts
Develop, evaluate, and implement physics-informed neural networks, neural operators, surrogate models, hybrid physics-machine-learning approaches, and generative models
Design and execute controlled experiments to train, test, and evaluate machine-learning models
Assess model accuracy, robustness, reliability, physics consistency, and generalization to conditions not represented in training data
Develop accelerated computational approaches that reduce the cost of full-physics simulations while maintaining fidelity to decision-relevant outputs
Translate physics and engineering requirements into machine-learning approaches, data representations, performance metrics, and validation protocols
Develop and maintain reproducible research software, technical documentation, and experimental records
Analyze quantitative results, identify model limitations and uncertainty, and communicate conclusions through reports, publications, briefings, and presentations

Requirements

What you’ll need

Minimum of 8 years with BS/BA; minimum of 6 years with MS/MA; minimum of 3 years with PhD
Ability to obtain and maintain a Secret security clearance
U.S. citizenship
Strong analytical, problem-solving, technical communication, and quantitative research skills
Demonstrated experience applying machine-learning or deep-learning methods to scientific, engineering, physics-based, or computational modeling problems
Experience developing, training, testing, and evaluating machine-learning models using PyTorch, TensorFlow, or JAX
Knowledge of neural-network optimization, regularization, representation learning, generative modeling, data management, model evaluation, and reproducible experimentation
Ability to formulate research problems, design controlled experiments, analyze results, identify model limitations, and communicate defensible technical conclusions
Ability to work independently while collaborating effectively with multidisciplinary technical teams and operational stakeholders
Ability to develop and maintain reproducible research software and documentation
Desired: Active Secret security clearance or higher
Desired: Experience with underwater acoustics, acoustic propagation, wave physics, signal processing, or related computational-physics applications
Desired: Experience with physics-informed machine learning, including physics-informed neural networks, neural operators, surrogate modeling, reduced-order modeling, or hybrid physics-machine-learning approaches
Desired: Experience developing surrogate models or emulators that accelerate computationally intensive physics-based simulations
Desired: Familiarity with uncertainty estimation, reliability assessment, and evaluating model generalization under distribution shift
Desired: Experience applying generative AI or large language models to scientific, engineering, or decision-support challenges
Desired: Experience collaborating with acousticians, physicists, engineers, scientists, or other multidisciplinary research teams
Desired: Ability to rapidly learn new scientific domains and translate domain-specific requirements into effective AI/ML workflows

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
Life insurance
Health savings account
Short-term disability
Long-term disability
Employee assistance program (EAP)
Parental leave
401(k)
Paid time off (PTO) for vacation
Company paid holidays
Eligible employees may be eligible for overtime
Shift differential
Discretionary bonus