Machine Learning Engineer, Intern (Term-Time)

Overview Powertown is hiring Machine Learning Engineers and Interns to build intelligence systems for battery storage and the electric grid. This role is about grid infrastructure, peak shaving, utility data, storage economics, asset behavior, forecasting, optimization, and agentic workflows. We are not looking for someone who simply trains models. We are looking for people who can invent new ways for AI, data, and software to make Powertown better at finding, evaluating, and operating storage assets. What You'll Do Build intelligence around peak shaving, load behavior, utility signals, and storage opportunitiesCreate agents that research, extract, compare, reason, and propose next stepsTurn grid data into decision systemsUse AI aggressively, then audit it ruthlesslyInvent workflows the industry does not yet have Preferred Majors Computer Science, Artificial Intelligence, Machine Learning, Data Science, Applied Math, Statistics, Electrical Engineering, Energy Systems, Operations Research, Power Systems, Physics, or another rigorous quantitative field. Ideal candidate You are a superstar technical thinker who uses ML as one of many toolsYou have very high agency. You do not wait for a clean dataset, a polished problem statement, or a perfect roadmap. You form hypotheses, build instruments, test ideas, and keep movingYou are AI-first but not AI-reliant. You use agents, LLMs, automation, and modern tools to move faster, but your own judgment stays in chargeYou are drawn to the grid because it is physical, messy, high-stakes, and full of hidden leverage Preferred Qualifications Evidence of exceptional ability: original research, serious side projects, open-source work, startup building, competitions, technical writing, or shipped systemsExperience with energy storage, utility data, tariffs, load analysis, peak shaving, grid infrastructure, electricity markets, forecasting, optimization, anomaly detection, agentic workflows, or AI-assisted research systemsExceptional ability exemplified through personal projects Compensation Range: $160K - $200K