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

Peraton; Mississippi; On-site; USD 104,000 - 166,000; Full time

Job summary

Peraton seeks an AI/ML Research Scientist to work onsite at the Stennis Space Center, focusing on underwater acoustics and physics-based ML methods. You will develop surrogate models, physics-informed networks, and generative approaches to accelerate naval-relevant simulations.

You will collaborate with multidisciplinary teams, design controlled experiments, and ensure reproducible research software and documentation.

Qualifications

• 8+ years with BS/BA; 6+ years with MS/MA; 3+ years with PhD.
• Ability to obtain and maintain a Secret security clearance
• U.S. citizenship required
• Strong analytical and quantitative research skills
• Experience applying ML/DL to scientific/engineering problems
• Proficiency with ML frameworks: PyTorch, TensorFlow, or JAX
• Knowledge of neural networks, regularization, generative modeling, reproducible experiments
• Ability to design controlled experiments and communicate results
• Ability to work independently and with multidisciplinary teams

Responsibilities

• Collaborate with acousticians, physicists, engineers, and other experts to improve ML approaches for complex problems
• Conduct research in deep learning, generative AI, LLMs for scientific applications
• Develop physics-informed neural networks, surrogate models, and hybrid physics-ML methods
• Design and run controlled experiments to train and evaluate models
• Assess model accuracy, robustness, and generalization to new conditions
• Create fast ML surrogates to reduce full-physics simulation costs
• Translate physics requirements into ML data representations and metrics
• Maintain reproducible research software and documentation
• Communicate conclusions via reports and briefings

Skills

Machine learning

Deep learning

Scientific computing

Python

Research

Education

PhD

MS/MA

BS/BA

Tools

PyTorch

TensorFlow

JAX

Job description

About Peraton

Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world's leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees solve the most daunting challenges that our customers face. Visit peraton.com to learn how we're keeping people around the world safe and secure.

About Peraton

Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world's leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees solve the most daunting challenges that our customers face. Visit peraton.com to learn how we're keeping people around the world safe and secure.

About The Role

Peraton is seeking an AI/Machine Learning Research Scientist to work onsite at the Stennis Space Center to conduct basic and applied research in machine learning, deep learning, generative artificial intelligence (AI), large language models, and physics-based machine learning. This role will develop and evaluate advanced computational methods for Navy-relevant scientific, engineering, acoustic, and decision-support challenges.

The successful candidate will focus primarily on underwater acoustics and acoustic propagation, with potential applications in wave physics, signal processing, and related computational-physics domains. The position will develop rapid machine-learning surrogate models for computationally demanding physics models while preserving decision-relevant fidelity, improving computational efficiency, and supporting reliable technical analysis.

What You Will Do

• Collaborate with acousticians, physicists, engineers, materials scientists, computer scientists, and other subject-matter experts to develop and improve machine-learning approaches for complex technical problems
• Conduct research in deep learning, generative AI, large language models, and scientific machine learning to support acoustic and physics-based applications
• 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 machine-learning surrogates and other accelerated computational approaches that reduce the cost of full-physics simulations while maintaining fidelity to decision-relevant outputs
• Translate physics and engineering requirements into practical machine-learning approaches, data representations, performance metrics, and validation protocols
• Develop and maintain clear, reproducible research software, technical documentation, and experimental records
• Analyze quantitative results; identify model limitations, uncertainty, and reliability considerations; and communicate technically sound conclusions through reports, publications, briefings, and presentations

Qualifications

What you will 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 a modern framework such as PyTorch, TensorFlow, or JAX
• Knowledge of core machine-learning concepts, including 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
• Experience with underwater acoustics, acoustic propagation, wave physics, signal processing, or related computational-physics applications
• Experience with physics-informed machine learning, including physics-informed neural networks, neural operators, surrogate modeling, reduced-order modeling, or hybrid physics-machine-learning approaches
• Experience developing surrogate models or emulators that accelerate computationally intensive physics-based simulations
• Familiarity with uncertainty estimation, reliability assessment, and evaluating model generalization under distribution shift
• Experience applying generative AI or large language models to scientific, engineering, or decision-support challenges
• Experience collaborating with acousticians, physicists, engineers, scientists, or other multidisciplinary research teams
• Ability to rapidly learn new scientific domains and translate domain-specific requirements into effective AI/ML workflows.

Details

Target Salary Range: $104,000 - $166,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual's experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.

Benefits Statement: Peraton offers eligible employees a variety of benefits including medical, dental, vision, life, health savings account, short/long term disability, EAP, parental leave, 401(k), paid time off (PTO) for vacation, and company paid holidays. A full listing of available benefits can be viewed at https://www.careers.peraton.com/benefits.

EEO:Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.