Founding Engineer, Applied AI

About Parisi Labs

Parisi Labs builds time-series foundation models and decision systems for complex operational environments. We are a small founding team, and the work is technical, early, and close to customers.

We need builders who can turn research direction and market learning into software that proves something real.

About the role

We are hiring a senior founding engineer who can move across applied AI systems, model-adjacent internal tools, data workflows, and early customer proof.

This is not a narrow platform role, a pure product role, or a management job. The job is to create durable software from the overlap of models, data, decisions, and early market learning.

What you will own

• Build applied AI systems around Parisi Labs' decisioning and time-series model roadmap.
• Turn research ideas into working prototypes, benchmarks, internal tools, and customer-proof artifacts.
• Build reusable systems from recurring forecasting, covariate search, data prep, readout, and evaluation work.
• Work across model code, data contracts, workflows, and lightweight internal or pilot-facing surfaces.
• Improve developer experience, observability, reliability, and deployment speed where those unlock more research or pilot throughput.
• Help set the technical bar for the first engineering hires.

You may be a fit if

• You still write code and want to be close to the work.
• You can work across model code, backend systems, internal tools, pilot surfaces, and customer proof without waiting for a perfect spec.
• You have strong technical taste and can explain tradeoffs clearly.
• You are comfortable with research-adjacent ambiguity and still care about shipping.
• You can say no to hyper-specific customer work while preserving the reusable technical insight.

Helpful background

• Senior engineering work at an AI startup, data company, infrastructure company, or frontier-model-adjacent product.
• Experience with Python, Type Script, Go, model-serving systems, data workflows, evaluation harnesses, or applied ML systems.
• Familiarity with APIs, gRPC, FastAPI-style services, Temporal, Kubernetes, GCP, Modal, or similar early-stage operating systems.
• Experience with DuckDB or Duck Lake-style analytical systems, object storage, queues, or warehouse-backed internal tools.
• Prior work turning prototypes into reliable, repeatable, or customer-credible systems.