Quick overview
Job description
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 opportunities
Create agents that research, extract, compare, reason, and propose next steps
Turn grid data into decision systems
Use AI aggressively, then audit it ruthlessly
Invent 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 tools
You 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 moving
You are AI-first but not AI-reliant. You use agents, LLMs, automation, and modern tools to move faster, but your own judgment stays in charge
You 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 systems
Experience with energy storage, utility data, tariffs, load analysis, peak shaving, grid infrastructure, electricity markets, forecasting, optimization, anomaly detection, agentic workflows, or AI-assisted research systems
Exceptional ability exemplified through personal projects
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