Peraton

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

Stennis Space Center, MS

Actively hiring, first seen by us today

Pay: $104,000–166,000/yearAnnual period assumed — pay source
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.
Read the full posting
Work setting: Not stated
Employment: Full-time — employment source
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting

Before you apply

Work authorization: Citizens onlyAbility to obtain and maintain a Secret security clearance

What you’ll work on

Full posting

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
Translate physics and engineering requirements into practical machine-learning approaches, data representations, performance metrics, and validation protocols

From the employer’s posting

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

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

What you’ll bring

All qualifications

Core experience

Ability to obtain and maintain a Secret security clearance
Experience with underwater acoustics, acoustic propagation, wave physics, signal processing, or related computational-physics applications
Demonstrated experience applying machine-learning or deep-learning methods to scientific, engineering, physics-based, or computational modeling problems
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, training, testing, and evaluating machine-learning models using a modern framework such as PyTorch, TensorFlow, or JAX
Experience developing surrogate models or emulators that accelerate computationally intensive physics-based simulations

Qualification wording

Ability to obtain and maintain a Secret security clearance

Experience with underwater acoustics, acoustic propagation, wave physics, signal processing, or related computational-physics applications

Demonstrated experience applying machine-learning or deep-learning methods to scientific, engineering, physics-based, or computational modeling problems

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, training, testing, and evaluating machine-learning models using a modern framework such as PyTorch, TensorFlow, or JAX

Experience developing surrogate models or emulators that accelerate computationally intensive physics-based simulations

Education & alternatives

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

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Posting record

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Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Nov 5, 2026today
Recorded sightings: 1

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

In line with similar jobs

$104K–$166K against $118K–$193K, the middle half of what similar US postings state.

From 3,721 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 0

Early. Similar jobs usually come down between day 7 and day 56.

At Peraton, jobs usually come down by about day 41.

From 10,297 similar postings that came down, and 245 of Peraton’s. Coming down is not always a hire.

Peraton is hiring

104 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Data Scientist hiring is picking up
Takes jobs down at a usual pace
7% of open jobs were reposted

Hiring record

See how this role fits your experience

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Where the posting names them

- 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

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Demand for Data Scientist roles

New Data Scientist postings at the employers we follow are down 3% over the last 5 weeks compared with the 5 before.

August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under Data Scientist, by the work the posting describes.

Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.

Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.

Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.

US government figures for data scientists

Median yearly pay, May 2025: $120,230
Expected change, 2025 to 2035: 34.6% more jobsagainst 3.5% for all jobs

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.

Job description

View original posting ↗

Peraton Overview

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 do the can’t be done by solving the most daunting challenges facing our customers. Visit peraton.com to learn how we’re keeping people around the world safe and secure.

Responsibilities

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.

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.

Application Statements

The application period for the job is estimated to be 30 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates. By applying to this job, you are expressing interest in the role and the Company. During the review of your application, you may be required to participate in an on-camera interview, as well as participate in a process to verify your identity. Use of artificial intelligence (AI) tools of any kind during Peraton interviews is strictly prohibited unless the candidate has obtained prior written authorization. All interview responses must be the candidate’s own.

EEO

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

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