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Remote Mechanical Engineering Machine Learning Jobs in Virginia

NT Concepts

Machine Learning Engineer

Herndon, VA · On-site +1

Vienna, VA and Chantilly, VA with remote flexibility Responsibilities: As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning applications and software systems ...

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site +1

Vienna, VA and Chantilly, VA with remote flexibility Responsibilities: As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning applications and software systems ...

Steampunk

Machine Learning Engineer

Mclean, VA · On-site +1

$115K - $150K/yr

We are looking for a more than just a "Machine Learning Engineer", but a technologist with ... Experience with data preprocessing, feature engineering, and model evaluation techniques.

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site +1

$140K - $150K/yr

... Machine Learning, Cyber Security and Cutting Edge Technology across the US Government. Be a part of ... Work closely with Data Engineering to align ML pipelines with the Bronze, Silver, Gold layers of a ...

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Remote Mechanical Engineering Machine Learning information

What are some typical challenges faced by remote mechanical engineers working with machine learning, and how can they be managed?

Remote mechanical engineers who work with machine learning often face challenges such as effective cross-functional collaboration, accessing and sharing large datasets, and keeping communication clear across distributed teams. To manage these, it's important to leverage collaborative tools for version control, data management, and regular virtual meetings. Building strong communication habits and proactively seeking feedback from data scientists, software engineers, and other stakeholders will help ensure project alignment and smooth workflows.

What is a Remote Mechanical Engineering Machine Learning job?

A Remote Mechanical Engineering Machine Learning job combines mechanical engineering expertise with machine learning techniques, allowing professionals to develop intelligent systems and optimize mechanical processes from a remote location. These roles often involve tasks such as analyzing engineering data, building predictive models, automating design tasks, and enhancing product performance using AI algorithms. Working remotely, engineers collaborate with teams through digital platforms, contributing to research, development, and deployment of machine learning solutions in mechanical engineering applications.

What is the difference between Remote Mechanical Engineering Machine Learning vs Remote Mechanical Engineering?

AspectRemote Mechanical EngineeringRemote Mechanical Engineering Machine Learning
Required CredentialsBachelor's or Master's in Mechanical EngineeringBachelor's or Master's in Mechanical Engineering; knowledge of Machine Learning
Work EnvironmentDesign, analysis, CAD modeling, testingDesign, analysis, CAD modeling with ML integration, data analysis
Industry UsageManufacturing, automotive, aerospaceManufacturing, automotive, aerospace with AI/ML applications
Common Search/ComparisonYesYes

Remote Mechanical Engineering involves traditional engineering tasks like design and analysis, while Remote Mechanical Engineering Machine Learning combines these with AI techniques to optimize processes and develop intelligent systems. The latter requires additional knowledge of machine learning but shares many core skills and industry applications.

What are the most commonly searched types of Mechanical Engineering Machine Learning jobs in Virginia? The most popular types of Mechanical Engineering Machine Learning jobs in Virginia are:
What are popular job titles related to Remote Mechanical Engineering Machine Learning jobs in Virginia? For Remote Mechanical Engineering Machine Learning jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Remote Mechanical Engineering Machine Learning jobs in Virginia look for? The top searched job categories for Remote Mechanical Engineering Machine Learning jobs in Virginia are:
What cities in Virginia are hiring for Remote Mechanical Engineering Machine Learning jobs? Cities in Virginia with the most Remote Mechanical Engineering Machine Learning job openings:
Remote Mechanical Engineering Machine Learning jobs near you
Machine Learning Modeling and Simulation Engineer

Machine Learning Modeling and Simulation Engineer

SAIC

Chantilly, VA • On-site, Remote

Full-time

Posted 11 days ago

SAIC rating

7.8

Company rating: 7.8 out of 10

Based on 78 frontline employees who took The Breakroom Quiz

68th of 203 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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