Parisi Labs

About Parisi Labs

Parisi Labs builds forecasting and decision systems for complex operational environments. Early customer work requires data access, baseline comparisons, workflow understanding, and clear readouts that show whether Parisi Labs is improving a real decision.

About the role

We are hiring a forward deployed engineer to take early customer problems from data access to baseline comparison to decision readout.

This is engineering-first. Early pilots will require messy customer data, custom source ingestion, lightweight internal tools, and judgment about which work should become reusable.

What you will own

• Translate customer workflows into data-access specs, implementation plans, baseline comparisons, and readout artifacts.
• Build pilot-specific integrations, adapters, analysis workflows, and internal tools.
• Identify which repeated pilot work should become reusable internal platform, data, or product infrastructure.
• Work with the CEO on buyer context and the CTO on technical feasibility.
• Make technical results legible to operators, executives, and internal researchers.
• Extract reusable connectors, eval templates, and risk logs from early deployments.

You may be a fit if

• You still write code and want to stay close to implementation.
• You are comfortable with messy customer data and ambiguous enterprise workflows.
• You can communicate technical results clearly to non-technical stakeholders.
• You can separate one-off customer work from reusable product or infrastructure.
• You have strong judgment about scope, credibility, and customer trust.

Helpful background

• Forward deployed engineering, customer engineering, implementation engineering, ML solutions engineering, data engineering, or applied AI engineering.
• Experience with Python, SQL, APIs, customer data, analytics, DuckDB-style analysis, or ML workflows.
• Experience with internal forecasting tools, APIs, notebooks, workflow prototypes, or benchmark/proof packets.
• Energy, industrial, finance, operations, or data-heavy enterprise experience.
• Experience writing code examples, technical docs, demos, notebooks, integration examples, or implementation guides.