<h2>About Parisi Labs</h2>Parisi Labs builds world models for physical industry, starting with power and energy markets. Products like Ask The Grid run on data that's large, messy, scattered and constantly changing. <h2>The role</h2>We're hiring an early-career data person who works natively with AI agents. You'll own the flow of data into our existing systems: sourcing it, ingesting it, cleaning it, keeping it accurate and extending it.We aren't looking for someone who sometimes asks ChatGPT for help. We want someone whose default way of working is to direct agents: building and running agent-driven workflows that ingest, clean and reconcile data at scale, and using agents to explore and find new sources we didn't know existed.<h2>What you'll own</h2><ul style=""><li>Ingest at scale: Use agents and LLM-powered pipelines to pull data from filings, utility and ISO documents, PDFs, websites, APIs and spreadsheets, then structure it.</li><li>Clean and reconcile: Normalize, de-duplicate, match entities and fix conflicts across sources, using automation instead of hand edits wherever possible.</li><li>Maintain and extend our systems: Keep existing ingestion pipelines and data stores healthy, add new sources and fields, and fix what breaks.</li><li>Explore and discover: Use agents to find new datasets and spot gaps, anomalies and patterns worth acting on.</li><li>Verify everything: Build checks so AI-generated data can be traced back to its source and trusted. Speed is only useful if the data is right.</li></ul><h2>What we're looking for</h2><ul style=""><li>You've built or run agent-driven or LLM-powered data workflows (for example extraction, scraping, classification or entity matching), and you can show how you checked the output.</li><li>You're comfortable working daily in tools like Claude Code, Cursor, MCP-connected agents or the OpenAI/Anthropic APIs.</li><li>Working Python and SQL. You can read, write and debug pipeline code even when an agent drafted it.</li><li>Strong judgment about data quality: you notice when something looks wrong and you go find out why.</li><li>High agency and comfort with ambiguity. You'd rather build the tool than repeat the task.</li><li>Students, recent grads and early-career people are encouraged to apply.</li></ul><h2>Nice to have</h2><ul style=""><li>Interest in energy, power markets or physical infrastructure</li><li>Experience with Airflow, dbt or similar orchestration tools</li><li>Startup or self-directed project experience</li></ul><h2>How to apply</h2>Share one example of real work you completed with AI agents or LLMs: what you built, how you ran it and how you verified the result. A link, repo or short write-up is fine. Finalists complete a short paid practical exercise, and using AI is encouraged.<h2>Compensation &amp; logistics</h2><ul style=""><li>$65–85K salary (full-time) or $30–40/hr (part-time/contract)</li><li>NYC or Boston/Cambridge, ~3 days/week onsite</li><li>Must be authorized to work in the US</li></ul>

Parisi Labs is an AI research and engineering company building world models for physical industry, starting with energy.We develop Metis, a family of models that learn how complex physical systems behave. Our work brings together data, models and tools to explore possible futures and understand how different actions could change outcomes.<a target="_blank" rel="noopener noreferrer nofollow" href="askthegrid.com">Ask The Grid</a> is our live system explorer for energy. It connects power markets, infrastructure, assets and weather in one place, putting our data and models to work. With operating partners, we are also developing Parisi Platform to connect models to real operational objectives and constraints.We are a small team working across machine learning research, data infrastructure and product engineering to improve how critical infrastructure is understood, operated and built.

AI-Native Data Associate - Energy Data

<h2>About Parisi Labs</h2>Parisi Labs builds world models for physical industry, starting with power and energy markets. Products like Ask The Grid run on data that's large, messy, scattered and constantly changing. <h2>The role</h2>We're hiring an early-career data person who works natively with AI agents. You'll own the flow of data into our existing systems: sourcing it, ingesting it, cleaning it, keeping it accurate and extending it.We aren't looking for someone who sometimes asks ChatGPT for help. We want someone whose default way of working is to direct agents: building and running agent-driven workflows that ingest, clean and reconcile data at scale, and using agents to explore and find new sources we didn't know existed.<h2>What you'll own</h2><ul><li>Ingest at scale: Use agents and LLM-powered pipelines to pull data from filings, utility and ISO documents, PDFs, websites, APIs and spreadsheets, then structure it.</li><li>Clean and reconcile: Normalize, de-duplicate, match entities and fix conflicts across sources, using automation instead of hand edits wherever possible.</li><li>Maintain and extend our systems: Keep existing ingestion pipelines and data stores healthy, add new sources and fields, and fix what breaks.</li><li>Explore and discover: Use agents to find new datasets and spot gaps, anomalies and patterns worth acting on.</li><li>Verify everything: Build checks so AI-generated data can be traced back to its source and trusted. Speed is only useful if the data is right.</li></ul><h2>What we're looking for</h2><ul><li>You've built or run agent-driven or LLM-powered data workflows (for example extraction, scraping, classification or entity matching), and you can show how you checked the output.</li><li>You're comfortable working daily in tools like Claude Code, Cursor, MCP-connected agents or the OpenAI/Anthropic APIs.</li><li>Working Python and SQL. You can read, write and debug pipeline code even when an agent drafted it.</li><li>Strong judgment about data quality: you notice when something looks wrong and you go find out why.</li><li>High agency and comfort with ambiguity. You'd rather build the tool than repeat the task.</li><li>Students, recent grads and early-career people are encouraged to apply.</li></ul><h2>Nice to have</h2><ul><li>Interest in energy, power markets or physical infrastructure</li><li>Experience with Airflow, dbt or similar orchestration tools</li><li>Startup or self-directed project experience</li></ul><h2>How to apply</h2>Share one example of real work you completed with AI agents or LLMs: what you built, how you ran it and how you verified the result. A link, repo or short write-up is fine. Finalists complete a short paid practical exercise, and using AI is encouraged.<h2>Compensation &amp; logistics</h2><ul><li>$65–85K salary (full-time) or $30–40/hr (part-time/contract)</li><li>NYC or Boston/Cambridge, ~3 days/week onsite</li><li>Must be authorized to work in the US</li></ul>

AI-Native Data Associate - Energy Data

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Parisi Labs

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