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We are seeking a motivated Machine Learning Intern to help design and test forecasting models that accelerate the decarbonization of the electricity grid. This role is ideal for students or recent graduates who want to apply their programming and analytical skills in a fast-paced environment, learn from experienced ML engineers, and contribute to solving real-world challenges in energy and climate. You will have the opportunity to work on cutting-edge problems in generative time-series forecasting, collaborate with a team of talented engineers and researchers, and see your ideas tested in real-world applications.
Requirements
- Currently pursuing or recently completed a BSc, MSc, or PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.
- Strong foundation in math, probability, statistics, and algorithms.
- Proficiency in Python and familiarity with ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn, numpy, pandas).
- Good understanding of data structures and software engineering principles.
- Strong analytical and problem-solving skills.
- Excellent communication skills and ability to collaborate in a team environment.
Nice to Have
- Previous internship, research, or project experience in machine learning, forecasting, or time-series modeling.
- Familiarity with energy systems, climate tech, or optimization problems.
- Contributions to open-source ML projects or personal ML research.
What You’ll Gain
- Hands-on experience developing ML models with direct impact on renewable energy integration.
- Mentorship from experienced ML engineers and researchers.
- Exposure to cutting-edge methods in generative forecasting and grid decarbonization.
- Opportunity to contribute to meaningful, climate-focused innovation.
Responsibilites:
- Assist in designing and implementing machine learning models for electricity grid forecasting.
- Explore and prototype ML algorithms for generative time-series forecasting.
- Support the extension and improvement of existing ML libraries and frameworks.
- Run experiments and analyze results to improve model performance.
- Help monitor and evaluate the performance of production models.
- Contribute to team discussions, brainstorming, and problem-solving.
Requirements:
- Currently pursuing or recently completed a BSc, MSc, or PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.
- Strong foundation in math, probability, statistics, and algorithms.
- Proficiency in Python and familiarity with ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn, numpy, pandas).
- Good understanding of data structures and software engineering principles.
- Strong analytical and problem-solving skills.
- Excellent communication skills and ability to collaborate in a team environment.
Nice to Haves:
- Previous internship, research, or project experience in machine learning, forecasting, or time-series modeling.
- Familiarity with energy systems, climate tech, or optimization problems.
- Contributions to open-source ML projects or personal ML research.
What You'll Gain:
- Hands-on experience developing ML models with direct impact on renewable energy integration.
- Mentorship from experienced ML engineers and researchers.
- Exposure to cutting-edge methods in generative forecasting and grid decarbonization.
- Opportunity to contribute to meaningful, climate-focused innovation.
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