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Autodesk Machine Learning Engineer Jobs (NOW HIRING)

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying ...

Roberts Recruiting

Machine Learning Engineer

Boston, MA · On-site

As a machine learning engineer, you will develop natural language processing systems that help our customers understand their contracts. You will work with a wide range of structured and unstructured ...

Autodesk

Machine Learning Engineering Manager

Atlanta, GA · On-site

$148K - $266K/yr

AI is the future of design and make, and Autodesk is pioneering the transformation of the AEC ... machine learning engineering or related field * 3+ years of experience as a manager * Experience ...

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying ...

Quantum Machines

Machine Learning Engineer

Boston, MA · On-site

Quantum Machines is a global leader in quantum computing control systems, and they are seeking a Machine Learning Engineer to design, build, and deploy machine learning systems for quantum processors.

Darwill, Inc.

Machine Learning Engineer

Villa Park, IL · On-site

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) Overview Darwill is a nationally recognized print and marketing communications firm based in the west suburbs of Chicago. As a premier provider of ...

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All JobsAutodesk Machine Learning Engineer Jobs

Autodesk Machine Learning Engineer information

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$31.5K

$128.8K

$193.5K

How much do autodesk machine learning engineer jobs pay per year?

As of Jun 7, 2026, the average yearly pay for autodesk machine learning engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What is the difference between Autodesk Machine Learning Engineer vs Data Scientist?

AspectAutodesk Machine Learning EngineerData Scientist
Required CredentialsBachelor's or higher in CS, AI, or related; experience with ML frameworksBachelor's or higher in CS, Statistics, or related; strong analytical skills
Work EnvironmentDesigning ML models for Autodesk products, collaborating with engineersAnalyzing data sets, building predictive models, providing insights
Industry UsagePrimarily in software development for design and engineering toolsAcross various industries including tech, finance, healthcare

While both roles involve working with data and algorithms, Autodesk Machine Learning Engineers focus on developing ML models specifically for Autodesk's software solutions, whereas Data Scientists analyze data to generate insights across multiple industries. The roles share similar educational backgrounds and technical skills but differ in their application and work environment.

Infographic showing various Autodesk Machine Learning Engineer job openings in the United States as of May 2026, with employment types broken down into 33% Internship, and 67% Full Time. Highlights an 33% In-person, and 67% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer

Waypoint Human Capital

Huntsville, AL

Other

Posted 7 days ago

Job description

Position Title: Machine Learning Engineer
Position Type: Full-time, On-Site
Location: Huntsville, AL
Clearance: Active TS
Description:
Waypoint's client is seeking a Machine Learning Engineer to support mission-critical efforts within a secure environment at the Missile and Space Intelligence Center. This role focuses on developing, integrating, and operationalizing machine learning solutions that support advanced analytics and intelligence capabilities.
The selected candidate will work across the full machine learning lifecycle, from building data pipelines and training models to deploying and monitoring production systems. This position requires a strong blend of software engineering and data science expertise, with a focus on scalability, performance, and system integration.
Responsibilities:
Integrate machine learning systems into existing software architectures and enterprise platforms
Design, build, and optimize data pipelines to support model training and inference
Develop, test, and deploy machine learning models into production environments
Manage transition from prototype to production, including deployment pipelines and monitoring solutions
Monitor model performance, including handling model drift, rollback, and failure scenarios
Conduct experiments and testing to evaluate and improve model accuracy and performance
Write clean, maintainable, and testable code in Python and related technologies
Collaborate with cross-functional teams to integrate ML capabilities into mission systems
Utilize CI/CD pipelines and GitOps practices to support automated deployment and version control
Support development in Linux and Windows environments
Required:
Active TS clearance (with ability to obtain TS/SCI with CI Polygraph)
Bachelor's degree in Computer Science, Mathematics, Statistics, Physics, or related technical field
Minimum 12+ years of overall experience, including 1-3 years working with machine learning frameworks
Strong programming skills in Python
Experience with machine learning frameworks, libraries, and data modeling techniques
Solid understanding of the machine learning lifecycle
Experience working with SQL and NoSQL databases
Experience working in Linux and Windows environments
Familiarity with CI/CD pipelines and Agile development methodologies
Understanding of software design and system integration principles
Desired:
Active TS/SCI with CI Polygraph (desired)
Experience working with large-scale (petabyte-level) datasets
Experience supporting multi-INT analytics environments
Experience deploying, monitoring, and scaling machine learning models in production
Experience with tools such as Docker, Jupyter Notebooks, PostgreSQL, GitLab, and GitHub
Experience implementing GitOps workflows
Experience working in secure or classified environment

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