Enphase Energy is a global energy technology company and leading provider of solar, battery, and electric vehicle charging products. Founded in 2006, Enphase transformed the solar industry with our revolutionary microinverter technology, which turns sunlight into a safe, reliable, resilient, and scalable source of energy to power our lives. Today, the Enphase Energy System helps people make, use, save, and sell their own power. Enphase is also one of the fastest growing and innovative clean energy companies in the world, with approximately 68 million products installed across more than 145 countries.
We are building teams that are designing, developing, and manufacturing next-generation energy technologies and our work environment is fast-paced, fun and full of exciting new projects.
If you are passionate about advancing a more sustainable future, this is the perfect time to join Enphase!
About the role
Assist in building AI/ML solutions for Enphase. The effort is to build AI-based end to end system features in order to leverage data, enhance decision making, and increase the speed of planning for Enphase. Enphase to succeed in market by building AI/ML application targeted for Solar Installers, Homeowners and Customer Support.
What you will do
- As an intern in the Enphase AI Team, you will be doing Research to AI model development including data procuring and analysis, prototyping, testing and deployment
- Working under supervision of members of AI team. Collaborate with data engineers for data acquisition from Enphase Digital Platform Products, collaborate with infrastructure engineer to deploy and model pipelines.
- Writing re-usable, easy-to-interpret code in Python(required), or other languages, while engaging in collaborative code reviews and providing technical feedback to improve existing ML model performance
- Establish scalable, efficient, automated process for large scale data analysis, model development, model validation and model implementation
Who you are and what you bring
- Currently enrolled in B.Tech in Computer Science or Electrical Engineering and >80% marks
-
Solid understanding of linear algebra, algorithms, machine learning, optimization, numerical methods, regression, classification, recommendations, anomaly detection, deep learning algorithms
- Excellent Python coding, strong engineering practices, debugging/profiling skills, familiarity with multi-threaded programming
- Experience with data science tools including Python scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks, bash scripting, Linux environment
- Knowledge in neural network computation optimization, such as distributed training, and network quantization
- Experience with at least one main stream deep learning frameworks as TensorFlow, PyTorch, Caffe(2), MXNet
- Familiar with attention, diffusion based and other GenAI approaches (preferably for computer vision applications)
- Demonstrable proficiency writing clean and concise code in Python
