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Fea Simulation Engineer Remote Jobs (NOW HIRING)

The Pennsylvania State University

Approval of remote and hybrid work is not guaranteed regardless of work location.For additional ... Simulation (USS) Department of the Model-Based Engineering (MBE) Division in the Undersea Systems ...

Cadence Inc

Principal Customer Service Engineer

Livonia, MI · Remote

$92K - $171K/yr

Lead delivery of FEA-based engineering consulting projects across multiple industries * Develop end-to-end simulation workflows tailored to customer needs * Perform advanced analyses including linear ...

Cisco

Senior Hardware Engineer- (Remote)

Atlanta, GA · On-site +1

$130K - $182K/yr

Develop and apply novel simulation and experimental methods to enable and optimize advanced ... FEA, CFD, coupled field analysis) and advanced packaging technology development. * Strong ...

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Fea Simulation Engineer Remote information

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How much do fea simulation engineer remote jobs pay per year?

As of Jun 12, 2026, the average yearly pay for fea simulation engineer remote in the United States is $123,399.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,000.00 and $146,500.00 per year, depending on experience, location, and employer.

What is a FEA Simulation Engineer?

A FEA (Finite Element Analysis) Simulation Engineer is a specialist who uses computer-aided engineering software to simulate and analyze how products, structures, or materials respond to physical forces such as stress, heat, vibration, and other real-world conditions. Their work helps predict potential issues, optimize designs, and ensure products meet safety and performance standards before manufacturing. Many FEA Simulation Engineers work remotely, collaborating with teams online and using advanced simulation tools to deliver results from any location.

What are the key skills and qualifications needed to thrive as a FEA Simulation Engineer (Remote), and why are they important?

To thrive as a FEA Simulation Engineer, you need a solid understanding of engineering principles, mechanics, and finite element analysis, typically supported by a degree in mechanical or related engineering fields. Expertise in FEA software such as ANSYS, Abaqus, or NASTRAN, and proficiency in CAD tools and scripting languages are commonly required, along with relevant certifications. Strong problem-solving abilities, attention to detail, and effective remote communication skills help set candidates apart in this role. These skills and qualities are essential for delivering accurate simulations, collaborating across distributed teams, and ensuring high-quality engineering solutions.

What is the difference between Fea Simulation Engineer Remote vs Mechanical Design Engineer?

AspectFea Simulation Engineer RemoteMechanical Design Engineer
CredentialsBachelor's or Master's in Mechanical, Aerospace, or Civil Engineering; proficiency in FEA softwareBachelor's or Master's in Mechanical or related engineering; CAD software skills
Work EnvironmentRemote, often collaborating via online toolsTypically on-site or hybrid, working in design labs or offices
Industry UsageCommon in automotive, aerospace, and manufacturing sectorsUsed across various industries including automotive, consumer products, and machinery

While both roles require engineering backgrounds and technical software skills, Fea Simulation Engineers primarily focus on analyzing and testing product durability remotely using simulation tools. Mechanical Design Engineers concentrate on creating and developing physical product designs, often working on-site. The key difference lies in their core responsibilities and work environments, with Fea Simulation Engineers more likely to work remotely and focus on simulation analysis.

What are some common challenges faced by remote FEA Simulation Engineers, and how can they be overcome?

Remote FEA Simulation Engineers often encounter challenges such as coordinating complex simulations across distributed teams and ensuring effective communication about project requirements and results. To overcome these, it's important to use collaborative tools for sharing models and results, maintain regular virtual meetings, and document simulation processes clearly. Proactively communicating progress and potential issues helps keep projects on track, and leveraging cloud-based simulation platforms can also streamline collaboration and computational resource sharing.
Machine Learning Modeling and Simulation Engineer

Machine Learning Modeling and Simulation Engineer

SAIC

Chantilly, VA • On-site, Remote

Full-time

Posted 23 days ago

SAIC rating

7.8

Company rating: 7.8 out of 10

Based on 78 frontline employees who took The Breakroom Quiz

71st of 204 rated it services

Job description

Job ID: 2611773

Location: Chantilly, VA, US

Date Posted: 2026-04-22

Category: Engineering and Sciences

Subcategory: Modeling/Sim Engr

Schedule: Full-Time

Shift: Day Job

Travel: No

Minimum Clearance Required: TS.SCI_wPoly

Clearance Level Must Be Able to Obtain: None

Potential for Remote Work: ORA_ON_SITE


Description

SAIC has need for a Machine Learning Modeling and Simulation Engineer  to support a rapidly expanding Government Intelligence Community (IC) customer with cutting-edge programs within the National Reconnaissance Office (NRO) in Chantilly, VA.

Note:  The role offers a flexible work schedule, but we ask our team to be available for team meetings during core business hours (10:00 a.m. – 3:00 p.m.).

As the Machine Learning Modeling and Simulation Engineer, you will provide technical expertise across a variety of Machine Learning (ML) and Modeling and Simulation (M&S) topics, including developing and training ML models, designing simulation frameworks, conducting performance analyses, and applying data-driven approaches to solve complex problems. You will also assist with Systems Engineering topics (e.g., requirements, configuration management, readiness, verification and validation, etc.) to ensure seamless integration of ML capabilities within simulation environments. 

Job Duties to include:

  • Develop and maintain physics-based simulation models of spacecraft systems, including structures, sensors, and mission environments.
  • Perform end-to-end performance modeling for satellite missions, integrating sensor, orbital, and environmental models.
  • Conduct sensor phenomenology studies, including optical, infrared, or radar modeling for detection, tracking, and signature analysis.
  • Perform orbital mechanics modeling including orbit determination, orbital maneuvering, and spacecraft flight dynamics.
  • Use scripting languages (Python, MATLAB, or similar) to automate workflows, perform data analysis, and interface between simulation tools.
  • Apply Artificial Intelligence/Machine Learning (AI/ML) techniques (e.g., supervised/unsupervised learning, reinforcement learning, predictive modeling) to enhance simulation fidelity and performance.
  • Develop AI/ML models to analyze and predict satellite system behaviors, performance metrics, and mission outcomes based on simulation data.
  • Design and implement algorithms for anomaly detection, predictive maintenance, and optimization of satellite operations.
  • Use statistical and machine learning techniques to analyze data, identify patterns, and uncover insights relevant to satellite systems.
  • Integrate AI/ML models into existing simulation frameworks and tools to enhance their capabilities.
  • Provide value-added judgment and offer strategic recommendations to the customer on program objectives, advanced technologies, and system enhancements. 
  • Produce highly detailed, practical, and consistent deliverables that align with the organization’s mission and objectives, with a focus on innovation and cutting-edge solutions in machine learning and simulation. 

Qualifications

Required Education and Experience:

  • Bachelor's Aerospace Engineering, Mechanical Engineering, Physics, and five (5) years or more experience; Masters and three (3) years or more experience; PhD and 0 years related experience. 
  • Active Top Secret/SCI w/Poly Clearance.
  • 3+ years of experience in modeling and simulation for aerospace or space systems.
  • Strong understanding of sensor phenomenology --such as optical, infrared, or radar systems --and associated modeling methods.
  • Intermediate Python programming experience, demonstrated through hands-on experience with tasks such as data manipulation, automation, and development of Python-based solutions. Experience with libraries such as NumPy, SciPy, pandas, and matplotlib is beneficial.
  • Ability to communicate technical results clearly in written and verbal formats.

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