
Machine Learning Engineer to Energy Efficiency Solutions
5 days ago
In this role, you will lead the development of advanced machine learning solutions while bridging academic research and real-world applications. Your key responsibilities include:
- Innovative Model Development: Researching and experimenting with cutting-edge AI approaches (deep learning, probabilistic modeling, signal processing, reinforcement learning, etc.) to tackle power system reliability challenges.
- Feature Engineering: Designing creative and robust features from diverse data sources (time-series, sensor data, SCADA signals, etc.) to uncover insights critical for fault detection and system optimization.
- Algorithmic Research & Validation: Keeping up with the latest academic literature, turning promising concepts into production-ready solutions, and validating performance through rigorous experimentation.
- Collaboration & Mentorship: Collaborating with team members to translate complex research into actionable solutions. Mentoring junior engineers and contributing to a culture of continuous learning.
- Scalable Deployment & Integration: Working with our software engineering team to integrate advanced AI models into Eneryield's platform, ensuring efficiency, scalability, and reliability in a cloud environment.
- Explainability & Transparency (XAI): Championing model interpretability, ensuring stakeholders understand the decision-making processes behind our AI-powered solutions.
Requirements
To succeed in this role, you should have:
- A PhD in AI & ML, Computer Science, Electrical Engineering, Applied Mathematics, or a related field (or equivalent experience).
- Proven experience conducting original research and driving AI/ML projects from concept to deployment.
- Deep understanding of machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
- Experience with advanced feature engineering, especially for time-series or sensor data.
- Strong programming skills in Python; familiarity with other languages is a plus.
- Ability to critically read and evaluate academic papers, translating findings into implementable solutions.
- A collaborative spirit, comfortable working in a fast-paced startup environment.
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