Machine Learning Engineer
Powertown Boston, Massachusetts, United States · $190K–$200K/yr
Utilities · 51-200 employees
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About the role
You will build intelligence systems for grid management, including peak shaving, load behavior analysis, and utility signal processing. You will also develop agentic workflows to research, extract, and reason through grid data to create novel decision systems.
What they look for
Requirements
The ideal candidate possesses a strong quantitative background in fields like Computer Science, AI, or Engineering and demonstrates high agency in technical problem-solving. You should have experience with energy storage, electricity markets, and modern AI tools while maintaining a focus on physical, high-stakes grid infrastructure.
Benefits
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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