Foundations ML Scientist - Time-Series & World Models

Parisi Labs, Inc.; Boston (MA); Hybrid; USD 210,000 - 275,000; Full time

Benefits offered by this job

Equity

Job summary

Parisi Labs, Inc. is hiring a researcher to develop, implement, and test core models for intelligent, foundation-scale systems in energy and related domains.

You will work closely with the co-founder and Chief Scientist to push the boundaries of time-series modeling and world modeling with rigorous experimentation. You will own independent research directions, manage end-to-end experiments, and collaborate with engineers to translate findings into production-ready components that inform

Qualifications

• 5+ years of combined post-graduate and industry ML research.
• Demonstrated research contributions with real impact.
• Deep expertise in time-series foundation models, world modeling, simulation, dynamical systems, and optimization.

Responsibilities

• Collaborate with the Chief Scientist to develop proprietary, state-of-the-art time-series and world models.
• Train and test models on difficult, real-world forecasting tasks.
• Own independent research directions from hypothesis to implementation and evaluation.
• Investigate multimodal inputs and physical constraint representations.
• Work with engineers to productionize research into decision-support systems.
• Extend advances to domains like supply chain, logistics, water, oil and gas, and manufacturing.

Skills

Time-series modeling

World modeling

Research & innovation

Engineering skills

Collaboration

Python

Education

PhD-level depth

Tools

Python

Research tooling

Simulation

Job description

Parisi Labs, Inc. is hiring a researcher to develop, implement, and test core models for intelligent, foundation-scale systems in energy and related domains.

You will work closely with the co-founder and Chief Scientist to push the boundaries of time-series modeling and world modeling with rigorous experimentation. You will own independent research directions, manage end-to-end experiments, and collaborate with engineers to translate findings into production-ready components that inform