Senior Full-Stack Product Engineer

Parisi Labs•New York, NY

3d•Onsite

About The Position

Parisi Labs is an AI research and product company building systems that understand how the physical world changes over time. We combine live data, forecasting, and interactive software so people can see what is happening, reason about what comes next, and make better decisions. Energy is our first proving ground. Ask The Grid is our live product for exploring the power system across markets, generation, demand, weather, and outages. It gives us a real environment in which to test models, ship useful tools, and learn from real users. We are a small team working across research, data, and product engineering. Everyone is expected to move from an ambiguous problem to a working system and to care whether the result is useful, reliable, and faithful to the underlying world. About the role Ask The Grid is already a substantial live product. We are looking for a senior product engineer to take technical ownership of it and keep making it faster, clearer, more reliable, and more useful. You will work across React and TypeScript, maps and time-series interfaces, APIs, accounts, data-intensive workflows, reliability, and AI-assisted product experiences. This is a product engineering role for someone who likes owning the whole outcome, not an ML research role. About Parisi Labs Parisi Labs is an AI company building learning systems for complex physical environments. We combine historical and live data with real operational context to help people understand the present, evaluate possible futures, and make better decisions. Energy is our first proving ground. Ask The Grid (https://askthegrid.com) is our public product for exploring the systems, markets, and assets that make up the power grid. We are a small technical team working across machine learning, data infrastructure, software, and real-world operations. About The Role We are hiring a senior full-stack product engineer to take substantial ownership of Ask The Grid and help us build the product experiences that follow from it. You will work across frontend, backend, data-backed workflows, and AI-assisted product features. The product is already live, so this is not a greenfield mockup role: you will learn from real usage, improve what exists, and ship durable software that makes a complex system understandable and useful. This role owns the user-facing product experience. It does not own the core machine-learning research agenda or the shared data platform.

Requirements

Production-quality software and strong product taste.
Ability to inherit a large existing application, understand it quickly, and improve it without reaching for a rewrite.
Care about architecture, tests, observability, performance, accessibility, and the small interaction details users feel.
Use AI tools aggressively without giving up correctness or clarity.
Ability to sit with a battery operator or grid planner and come back with working software, not only a specification.
Comfortable saying no to work that will not generalize.
TypeScript and React in production; strong API and database judgment; comfort in Python where the data lives.
Experience with maps, charts, time-series visualization, or interfaces over large live datasets.
Experience with authentication, organizations, permissions, billing, reliability, and production operations.
Startup or high-ownership experience is strongly preferred.

Nice To Haves

Experience integrating LLMs or agents is useful but not required.
Energy experience is welcome and not required.

Responsibilities

The public surfaces: maps, radar, assets, network views, and how they work together as one dependable product.
The path from visitor to operator: accounts and organizations, saving and claiming assets, portfolios, private-data connection, permissions, alerts, and reports.
Product architecture, APIs, performance, observability, reliability, accessibility, and the ordinary systems that make ambitious software dependable.
The interaction quality of the agent inside the product: how context is selected, how sources are shown, and what happens when the system should refuse.
The judgment call about what becomes reusable software, what remains an operator-specific workflow, and what should not be built.
Ship end-to-end product improvements across the application, from interface through backend behavior and release.
Make complex, changing data legible through clear interactions, visualizations, and workflows.
Improve the ways people explore, save, organize, and act on the information that matters to them.
Build AI-assisted features that are useful, trustworthy, and clear about their limits.
Use product evidence to find friction, set priorities with the founders, and measure whether a change worked.
Raise the bar for performance, reliability, accessibility, and maintainability across the product.
Help decide which repeated needs should become reusable product capabilities.