Machine Learning Engineer, Intern (Term-Time)

Powertown; Boston; On-site; PHP 6,270,000 - 11,285,000; Full time

Job summary

Powertown is seeking Machine Learning Engineers and Interns to build intelligence systems for battery storage and the electric grid. You will work on peak shaving, utility data, and forecasting to invent new AI-enabled workflows that optimize operating storage assets.

You will design agents to research, extract, compare, and propose next steps, turning raw grid data into actionable decisions. We expect you to actively use LLMs and automation, while maintaining strong judgment and rigorous audits

Qualifications

• Evidence of exceptional ability: original research, side projects, OSS work, startup building, or shipped systems.
• Experience with energy storage, utility data, tariffs, load analysis, peak shaving, grid infrastructure, forecasting or optimization.
• Ability to design AI-assisted research systems and agentic workflows for complex grid problems.

Responsibilities

• 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

Skills

ML engineering

Energy systems

Forecasting

Agentic workflows

Education

Bachelor's degree in Computer Science / AI / ML / Data Science

Tools

LLMs

Automation tools

Agent-based systems

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