Machine Learning Engineer
Required skills
- peak shaving
- load behavior
- utility signals
- storage opportunities
- agents
- extract
- compare
- reason
- propose
- grid data
Nice to have
- original research
- side projects
- open-source work
- startup building
- competitions
- shipped systems
Role summary
We are seeking a Machine Learning Engineer to build intelligence around energy systems, focusing on peak shaving, load behavior, utility signals, and storage opportunities. You will create agents that research, extract, compare, reason, and propose next steps, turning grid data into decision systems. The role requires aggressive use of AI, followed by rigorous auditing, and invention of novel industry workflows. The ideal candidate is a superstar technical thinker with high agency, comfortable using AI tools while maintaining their own judgment. Experience in energy storage, utility data, forecasting, optimization, or anomaly detection is preferred.
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
