AI Research Scientist - Energy Foundation Model

Fuse Energy; New York (NY); Presencial; USD 150.000 - 210.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive equity

Biannual bonus scheme

Fully expensed tech gear

Private health insurance

Breakfast and dinner allowance

Descripción de la vacante

Fuse Energy is pursuing ambitious AI research to power an Energy Foundation Model that unifies generation, grid, demand, weather, and price forecasting. The role focuses on pre-training the model, defining inputs and training objectives, and validating improvements with rigorous experiments and backtests.

You will work with our quants and software engineers to translate research into production, leveraging household-level data while protecting privacy.

Formación

• Do the pre-training research for the Energy Foundation Model.
• Design the architecture, inputs and training objectives for one model across generation, grid, demand, weather and price data.
• Run pre-training experiments and find what actually improves results.
• Write up findings as research, and work with quants and engineers who take the model into production.

Responsabilidades

• Do the pre-training research for the Energy Foundation Model.
• Design the architecture, inputs and training objectives for one model across generation, grid, demand, weather and price data.
• Run pre-training experiments and find what actually improves results.
• Build rigorous evaluations and honest backtests, with calibrated uncertainty.
• Train on household-level data without leaking personal information.
• Write up findings as research, and work with our quants and engineers who take the model into production.

Conocimientos

Pre-training research

Model architecture design

Experimentation & backtesting

Cross-functional collaboration

Descripción del empleo

Fuse Energy is pursuing ambitious AI research to power an Energy Foundation Model that unifies generation, grid, demand, weather, and price forecasting. The role focuses on pre-training the model, defining inputs and training objectives, and validating improvements with rigorous experiments and backtests.

You will work with our quants and software engineers to translate research into production, leveraging household-level data while protecting privacy.