Build ML and optimization models driving real-time energy trading decisions for a PE-backed renewable energy company with 9GW of projects in development. Earn $150k–$200k/yr as a principal engineer working on high-impact problems at the intersection of AI and clean energy.
You'll design and implement MILP optimization models for energy dispatch and trading, build predictive pricing models using machine learning, integrate forecasting and market data into real-time trading workflows, optimize algorithm performance for scale and latency requirements, and collaborate with trading and operations teams on model deployment.
You need a PhD in operations research, mathematics, physics, or related field, plus 5+ years building production optimization and ML systems. Strong proficiency in Python, Julia, or similar languages is essential. You should have experience with optimization solvers, energy markets knowledge is a plus but not required.
