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Machine Learning Engineer, Intern

Powertown Boston, Massachusetts, United States · $104K–$125K/yr

Utilities · 51-200 employees

2 d ago
machine-learning Junior (0-2 yrs) Full-time United States
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About the role

You will build intelligence systems for grid management, including peak shaving and load behavior analysis. You will also create agentic workflows to extract, reason, and propose solutions using grid data.

What they look for

Machine Learning Artificial Intelligence Data Science Python Forecasting Optimization Anomaly Detection Agentic Workflows Energy Storage Grid Infrastructure Electricity Markets Load Analysis Peak Shaving Utility Data Statistics Applied Math

Requirements

Candidates should be pursuing or hold a degree in a quantitative field such as Computer Science, Engineering, or Physics. You must demonstrate exceptional technical ability through research, side projects, or open-source contributions.

Benefits

Equity

Full description

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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