Founding Engineer for ML Systems & Forecasting

Parisi Labs; New York (NY); Hybrid; USD 225,000 - 280,000; Full time

Benefits offered by this job

Meaningful equity

Job summary

Parisi Labs in New York City or Boston/Cambridge is seeking an engineer who can turn 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 the founders and researchers.

Your job is to shorten the path from hypothesis to reliable software, and to turn model outputs into useful forecasting, scenario-exploration, and decision-support capabilities.

Qualifications

• Proven track record delivering production ML or agent-based systems.
• Strong backend, API, data workflow, testing, and debugging skills.
• Experience turning research into reliable software and tools.
• Ability to collaborate with researchers and founders.
• Good judgment on when to build reusable infrastructure vs quick solutions.
• Clear communication with cross-functional teams.

Responsibilities

• Turn research ideas into working prototypes, evaluations, and customer-ready capabilities.
• Build forecasting, data preparation, inference, evaluation, and decision-support workflows.
• Develop backend services and internal tools used by product teams.
• Investigate model and system failures and improve evaluations.
• Collaborate with CTO, Chief Scientist, and CEO on technical direction.
• Set engineering standards including testing and documentation.

Skills

Production ML

Backend services

APIs

Data workflows

Testing & debugging

Collaboration

Tools

Cloud services

Job description

Parisi Labs in New York City or Boston/Cambridge is seeking an engineer who can turn 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 the founders and researchers.

Your job is to shorten the path from hypothesis to reliable software, and to turn model outputs into useful forecasting, scenario-exploration, and decision-support capabilities.