Founding AI Engineer — Build Real-World Forecasting Systems

Worky; New York (NY); On-site; USD 225,000 - 290,000; Full time

Job summary

Parisi Labs is seeking an engineer to translate 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 back into the next experiment. This is not a narrow ML infrastructure role or a pure research role.

Qualifications

• 5+ years of senior engineering experience in AI/ML systems.
• Strong Python and at least one production language (TypeScript or Go).
• Experience with model-serving systems, data pipelines, and evaluation harnesses.
• Familiarity with APIs, gRPC, and Kubernetes.
• Proven ability to ship reliable software from research ideas.

Responsibilities

• Turn research ideas into working prototypes, tools, and customer-ready capabilities.
• Build forecasting data workflows, evaluation harnesses, and inference pipelines.
• Develop backend services and internal tools that scale with product needs.
• Collaborate with CTO, Chief Scientist, and CEO on architecture and direction.
• Improve developer experience, observability, reliability, and security.

Skills

Python

TypeScript

Go

Model-serving

Data workflows

Evaluation harnesses

APIs / gRPC

Kubernetes

Testing / observability

Security / reliability

Tools

Kubernetes

gRPC

FastAPI

Temporal

Google Cloud Platform

Modal

Job description

Parisi Labs is seeking an engineer to translate 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 back into the next experiment. This is not a narrow ML infrastructure role or a pure research role.