Founding Engineer, Applied AI

Parisi Labs Engineer

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.

We are looking for an engineer who can turn new modeling and agent ideas into working systems. You will build evaluations, data and inference workflows, backend services, internal tools, and product-facing experiments while working directly with founders and researchers.

Your job is to shorten the path from hypothesis to reliable software and bring failures observed in real products and real data back into the next experiment. This is not a narrow ML infrastructure role, a pure research role, or a management job.

What you will own:

• Turn research ideas into working prototypes, evaluations, internal tools, and customer-ready capabilities.
• Build reusable systems for forecasting, covariate search, data preparation, evaluation, and decision support.
• Work across model code, APIs, data contracts, workflows, backend services, and lightweight product surfaces.
• Improve developer experience, observability, reliability, security, and deployment speed where they unlock more research and product throughput.
• Collaborate directly with the CTO, Chief Scientist, and CEO on product and technical direction.
• Help set the technical standard for the engineering team we are building.

First 90 days:

In your first month, you will understand Ask The Grid, the modeling stack, and the data plane and identify one high-leverage system to own. By day 60, you will ship a working capability that converts research, data, or customer learning into repeatable software. By day 90, you will own a durable system surface and materially reduce repeated founder build work.

You may be a fit if:

• You are a senior hands-on engineer who wants to stay close to the code.
• You can move between model-adjacent code, backend systems, data workflows, internal tools, and product surfaces without waiting for a perfect specification.
• You have strong technical judgment and can explain tradeoffs clearly.
• You are comfortable with research ambiguity and care deeply about shipping reliable software.
• You can distinguish reusable technical capability from one-off customer work.
• You want to help define both the architecture and engineering culture of an early technical company.

Helpful background:

• Senior engineering work at an AI startup, data company, infrastructure company, or research-adjacent product.
• Strong experience with Python and at least one production-oriented language or product stack such as Type Script or Go.
• Experience with model-serving systems, data workflows, evaluation harnesses, or applied ML.
• Familiarity with APIs, gRPC, FastAPI-style services, Temporal, Kubernetes, GCP, Modal, or comparable infrastructure.
• Experience with analytical data systems, object storage, queues, or warehouse-backed tools.
• A record of taking prototypes into reliable, repeatable, customer-credible operation.
• High standards for testing, observability, security, reliability, and deployment without overbuilding.

Location and working style:

This is a founder-close, high-context role. New York City is preferred. Boston/Cambridge or Westchester can work with a regular in-person cadence in New York.

Compensation:

The base salary range is $225,000-$290,000 per year, plus meaningful early equity. Final compensation depends on experience, location, seniority, and role scope.

Interview process:

The process is a founder screen, an all-founder interview, a practical trial task based on a real Parisi Labs system problem, an in-person final, and offer review.

Why join now:

You will help turn new research ideas, live data, and product evidence into useful systems for understanding the physical world and making better decisions.