About Parisi LabsParisi Labs is building foundational world models for physical industry. We are developing models that learn how complex physical systems behave and reuse that understanding across forecasts, scenarios, and operational decisions.Energy is our first proving ground. We combine historical and live data with operational context, bringing together machine learning research, data infrastructure, and software engineering to turn advances in modeling into useful technology for energy operators.We are a small technical team working directly with the founders on our core models, systems, and products.About the roleWe are looking for an ML Data Platform Engineer to make the data behind our models, products, and customer deployments dependable, understandable, and easy to use.This role sits where data engineering meets machine learning. You will turn messy, changing real-world sources into durable datasets and interfaces that researchers and engineers can trust. Your work will support both public data and customer-authorized operational data.The goal is not to build a large platform for its own sake. It is to make each new model, product capability, and data source faster to bring online without compromising correctness. You will own the shared data foundations, working with the applied-AI engineer on model requirements and the product engineer on application needs.What you'll own<ul style=""><li>Build and improve ingestion, backfills, validation, and observability for high-volume, time-dependent data.</li><li>Define clear data contracts and point-in-time semantics for model training, evaluation, and product use.</li><li>Create reusable workflows for bringing public and customer-authorized sources into the system.</li><li>Build quality, lineage, freshness, and access controls that make data trustworthy in repeated use.</li><li>Develop efficient datasets and query interfaces for machine-learning and product workloads.</li><li>Diagnose whether failures originate in source data, transformations, model inputs, or serving systems.</li><li>Work with researchers and engineers to turn recurring data requirements into reliable software rather than manual projects.</li><li>Decide which abstractions should become shared infrastructure and which should remain purpose-built.</li></ul>What we're looking for<ul style=""><li>A record of designing, building, and operating production data systems that researchers or engineers depend on.</li><li>Strong programming, querying, and data-modeling skills, with the ability to write maintainable, tested production software.</li><li>An understanding of time-dependent data correctness, including backfills, revisions, freshness, and point-in-time availability.</li><li>Experience supporting ML training and evaluation, or similarly demanding data-intensive product workloads.</li><li>The ability to design practical data contracts, validation, observability, and access controls without overbuilding the platform.</li><li>Strong debugging and collaboration skills, including the ability to trace failures across systems and explain data limitations clearly.</li></ul>First 90 days<ul style=""><li>30 days: Understand the data lifecycle behind Ask The Grid and our ML work, and identify the most consequential reliability and usability gaps.</li><li>60 days: Ship a reusable ingestion, backfill, validation, or dataset capability used in active product or research work.</li><li>90 days: Own a dependable end-to-end data workflow, with documented contracts, quality checks, and clear operational visibility.</li></ul>Why joinThe quality of our models and products depends on the quality of their underlying data. You will shape that foundation early, working directly with researchers and engineers who use it, and see your work support new experiments, product capabilities, and customer deployments.Location and compensationLocation: New York City or Boston/Cambridge. This is a hybrid role — we expect in-person collaboration 3 days per week in person.Salary range: $175,000–$245,000 USD.Equity: meaningful early-company equity.

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.

Founding Research Engineer

About Parisi LabsParisi Labs is building foundational world models for physical industry. We are developing models that learn how complex physical systems behave and reuse that understanding across forecasts, scenarios, and operational decisions.Energy is our first proving ground. We combine historical and live data with operational context, bringing together machine learning research, data infrastructure, and software engineering to turn advances in modeling into useful technology for energy operators.We are a small technical team working directly with the founders on our core models, systems, and products.About the roleWe are looking for an ML Data Platform Engineer to make the data behind our models, products, and customer deployments dependable, understandable, and easy to use.This role sits where data engineering meets machine learning. You will turn messy, changing real-world sources into durable datasets and interfaces that researchers and engineers can trust. Your work will support both public data and customer-authorized operational data.The goal is not to build a large platform for its own sake. It is to make each new model, product capability, and data source faster to bring online without compromising correctness. You will own the shared data foundations, working with the applied-AI engineer on model requirements and the product engineer on application needs.What you'll own<ul><li>Build and improve ingestion, backfills, validation, and observability for high-volume, time-dependent data.</li><li>Define clear data contracts and point-in-time semantics for model training, evaluation, and product use.</li><li>Create reusable workflows for bringing public and customer-authorized sources into the system.</li><li>Build quality, lineage, freshness, and access controls that make data trustworthy in repeated use.</li><li>Develop efficient datasets and query interfaces for machine-learning and product workloads.</li><li>Diagnose whether failures originate in source data, transformations, model inputs, or serving systems.</li><li>Work with researchers and engineers to turn recurring data requirements into reliable software rather than manual projects.</li><li>Decide which abstractions should become shared infrastructure and which should remain purpose-built.</li></ul>What we're looking for<ul><li>A record of designing, building, and operating production data systems that researchers or engineers depend on.</li><li>Strong programming, querying, and data-modeling skills, with the ability to write maintainable, tested production software.</li><li>An understanding of time-dependent data correctness, including backfills, revisions, freshness, and point-in-time availability.</li><li>Experience supporting ML training and evaluation, or similarly demanding data-intensive product workloads.</li><li>The ability to design practical data contracts, validation, observability, and access controls without overbuilding the platform.</li><li>Strong debugging and collaboration skills, including the ability to trace failures across systems and explain data limitations clearly.</li></ul>First 90 days<ul><li>30 days: Understand the data lifecycle behind Ask The Grid and our ML work, and identify the most consequential reliability and usability gaps.</li><li>60 days: Ship a reusable ingestion, backfill, validation, or dataset capability used in active product or research work.</li><li>90 days: Own a dependable end-to-end data workflow, with documented contracts, quality checks, and clear operational visibility.</li></ul>Why joinThe quality of our models and products depends on the quality of their underlying data. You will shape that foundation early, working directly with researchers and engineers who use it, and see your work support new experiments, product capabilities, and customer deployments.Location and compensationLocation: New York City or Boston/Cambridge. This is a hybrid role — we expect in-person collaboration 3 days per week in person.Salary range: $175,000–$245,000 USD.Equity: meaningful early-company equity.

Founding Research Engineer

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Parisi Labs

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