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Machine Learning Engineer Energy 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 ...

Alignerr

Machine Learning Engineer

Manhattan, NY · Remote

$50 - $70/hr

Machine Learning Engineer - AI Data Trainer Location: Remote About The Job At Alignerr, we partner with the world's leading AI research teams and labs to build and train cutting‐edge AI models.

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 ...

PhysicsX

Machine Learning Engineer

San Francisco, CA · On-site

Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors ... Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who ...

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.

Compunnel

Machine Learning Engineer

Freeport, ME · Remote

Machine Learning Engineer Location: Freeport Maine Remote Must haves: * 4+ years ML experience * Python / Spark * Tensorflow / PyTorch (or similar) * Databricks * MLflow * Docker * SQL * Design and ...

Root Access Inc

Machine Learning Engineer

New York, NY · On-site

The Machine Learning Engineer will be responsible for designing and developing machine learning ... You don't need permission-you need a challenge. * 1-of-1 Energy - You've been underestimated, or ...

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 excellent communication and customer service skills and a passion for data and problem solving.

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Machine Learning Engineer Energy information

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$31.5K

$128.8K

$193.5K

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

As of Jun 6, 2026, the average yearly pay for machine learning engineer energy 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 are the key skills and qualifications needed to thrive as a Machine Learning Engineer in the energy sector, and why are they important?

To thrive as a Machine Learning Engineer in the energy sector, you need strong programming skills (Python, R), a solid background in mathematics and statistics, and typically a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), data analytics platforms, and industry-specific tools like SCADA systems is often required. Excellent problem-solving, collaboration, and communication skills help you translate complex data insights into actionable solutions for energy operations. These competencies enable you to develop effective models that optimize energy systems, drive innovation, and support critical decision-making in a highly technical industry.

What does a Machine Learning Engineer do in the energy sector?

A Machine Learning Engineer in the energy sector develops and deploys algorithms to analyze large datasets, optimize energy systems, and improve efficiency. They may work on predictive maintenance for equipment, demand forecasting, energy consumption analysis, and integrating renewable sources. Their work helps energy companies make data-driven decisions, reduce costs, and support sustainability goals by leveraging advanced machine learning techniques.

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

AspectMachine Learning Engineer EnergyData Scientist
CredentialsBachelor's/Master's in CS, Data Science, or related; experience with ML frameworksBachelor's/Master's in Statistics, Math, or CS; strong analytical skills
Work EnvironmentEnergy sector projects, renewable energy, utilitiesVarious industries including finance, healthcare, tech
Employer & Industry UsageEnergy companies, utilities, renewable firmsBroad industry application across sectors
Search & Comparison IntentFocus on ML applications in energy sectorBroader data analysis and modeling roles

While both roles require strong analytical skills and experience with data tools, Machine Learning Engineers Energy focus on developing and deploying ML models specifically for energy-related applications, whereas Data Scientists analyze data across various industries to generate insights. The roles overlap in skills but differ in industry focus and project scope.

What are some unique challenges Machine Learning Engineers face in the energy sector, and how can they prepare to address them?

Machine Learning Engineers in the energy sector often encounter challenges related to working with large, complex, and sometimes incomplete datasets from sources like smart grids, sensors, or renewable energy systems. Additionally, they must ensure that models are robust enough to handle real-time data and changing operational conditions. Collaborating closely with domain experts, such as energy analysts and engineers, is crucial for understanding the nuances of the data and ensuring that solutions are practical and compliant with industry regulations. Gaining familiarity with industry-specific software and data protocols, as well as developing strong communication skills, can help candidates excel in this role.

Machine Learning Engineer

Waypoint Human Capital

Huntsville, AL • On-site

Other

Posted 6 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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