Create the high-level optimization frameworks (MILP, NLP, or Stochastic Programming) to manage residential energy flows across heat pumps, thermal storage, EVs, and batteries., Design and tune closed-loop control strategies to ensure system stability, robustness against model/reality mismatch, and seamless integration of high-level optimization with devices constraints., Utilize Stochastic & Learning-Based Control (e.g. Markov Decision Processes (MDPs), Reinforcement Learning, or Model Predictive Control (MPC)) to handle the uncertainty of weather, prices, and human behavior., Develop ML models that respect real-world constraints. You ensure our algorithms "understand" the thermal inertia of a building or the degradation curves of a lithium-ion battery., Build high-fidelity simulations to validate algorithm performance against historical data before deploying code to edge devices and cloud environments., You don’t just write formulas; you architect and implement complex models from scratch in Python, ensuring they are robust enough to run in a cloud-to-edge environment., Act as a senior voice in technical sessions. You will mentor junior team members and help navigate complex problem-solving and define the algorithmic requirements that guide our product roadmap., Work closely with Energy Engineers and Backend Developers to translate math into reliable, production-grade services that save customers money and CO2.
