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Renewable Energy Machine Learning Engineer Jobs

Machine Learning Engineer

Manhattan, NY · On-site

$170.17 - $255.26/hr

Job Overview Machine Learning Engineer w/ Spotify USA Inc. in NY, NY. Bld productn systms that enrich & improve Spotify listeners' exp on Spotify usg machine learng techniques. Bach deg (U.S. or for ...

New

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 ...

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 ...

Job Title Machine Learning Engineer Location Remote Rate $48/hr on W2 Must Haves: Neaural networks NLP Python AZURE Pytorch or tensorflow Machine Learning Engineer / AI Engineer Role Role Overview ...

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 ...

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

The Role We are looking for a Machine Learning Engineer to bridge the gap between AI research and production-grade flight systems. You will optimize, deploy, and scale machine learning models that ...

Develop platform-level tools for prompt engineering, automated evaluation, prompt optimization, and experimentation. * Deploy, monitor, and maintain machine learning and generative AI models in ...

New

Machine Learning Engineer We're looking for a talented and motivated Machine Learning Engineer to join our team and help develop cutting-edge AI solutions. In this role, you'll have the opportunity ...

Showing results 41-60

Renewable Energy Machine Learning Engineer information

See salary details

$31.5K

$128.8K

$193.5K

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

As of Aug 8, 2026, the average yearly pay for renewable energy 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 does a renewable energy machine learning engineer do?

A Renewable Energy Machine Learning Engineer develops and applies machine learning algorithms to optimize renewable energy systems such as solar, wind, or hydroelectric power. Their work often involves analyzing large datasets from energy sources, predicting energy production, improving efficiency, and supporting smart grid management. These engineers collaborate with data scientists, energy analysts, and hardware engineers to create innovative solutions that advance the adoption and reliability of renewable energy technologies.

What are the key skills and qualifications needed to thrive as a renewable energy machine learning engineer, and why are they important?

A Renewable Energy Machine Learning Engineer needs a strong background in machine learning, data analysis, and renewable energy systems, typically supported by a degree in engineering, computer science, or a related field. Familiarity with programming languages like Python, machine learning frameworks such as TensorFlow or PyTorch, and energy sector datasets or simulation tools is essential. Strong problem-solving, communication, and teamwork skills help in translating data insights into actionable solutions and collaborating with multidisciplinary teams. These skills drive innovation and optimize energy production, making significant impacts on sustainability and efficiency in the renewable energy sector.

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

AspectRenewable Energy Machine Learning EngineerData Scientist in Renewable Energy
CredentialsDegree in Engineering, Computer Science, or related field; knowledge of ML frameworksDegree in Data Science, Statistics, or related field; strong analytical skills
Work EnvironmentDevelops ML models for renewable energy systems, often in engineering teamsAnalyzes data to inform renewable energy projects, often in research or analytics teams
Industry UsageDesigns ML solutions for wind, solar, or hydro energy systemsInterprets data to optimize renewable energy production and efficiency

The main difference is that Renewable Energy Machine Learning Engineers focus on developing and implementing ML models specifically for renewable energy systems, while Data Scientists analyze data to support decision-making in renewable energy projects. Both roles require strong technical skills, but the engineer's role is more focused on model deployment within energy systems.

How does a renewable energy machine learning engineer typically collaborate with cross-functional teams to implement data-driven solutions?

Renewable Energy Machine Learning Engineers work closely with data scientists, energy analysts, software developers, and project managers to design and deploy predictive models that optimize energy production and distribution. Collaboration often involves translating complex technical findings into actionable insights for non-technical stakeholders and integrating machine learning outputs into existing energy management systems. Effective communication and teamwork are essential, as engineers must ensure their models align with operational goals and regulatory requirements. This collaborative environment fosters innovation and allows engineers to see the direct impact of their work on sustainable energy initiatives.
Infographic showing various Renewable Energy Machine Learning Engineer job openings in the United States as of August 2026, with employment types broken down into 80% Full Time, and 20% Nights. Highlights an 100% In-person job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer

AI Squared

Washington, DC

Full-time

Re-posted 18 days ago


Job description

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, maintaining, and monitoring the AI/ML systems that power our platform. You will work closely with data scientists, data engineers, and product teams to ensure scalable, reliable, and production-grade AI solutions. You'll play a critical role in operationalizing large language models (LLMs) and other ML systems, ensuring they run efficiently, securely, and with robust monitoring in place.

Key Responsibilities:
  • Design, implement, and maintain ML deployment pipelines for scalable production systems.
  • Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
  • Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
  • Partner with data scientists to transition models from research/prototype into production-ready deployments.
  • Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
  • Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
  • Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
  • Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.
Qualifications:
  • 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
  • Proven experience deploying and maintaining machine learning models in production at scale.
  • Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
  • Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
  • Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
  • Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
  • Strong understanding of MLOps best practices, monitoring, and automation.
  • Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
  • Strong communication and collaboration skills across technical and non-technical teams.