Research Engineer

Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.

Our base salary range is $146,880 - $220,320 and in addition we have generous bonus plans to provide a competitive compensation package. 

We will begin reviewing applications on October 19, 2026.

Who You Are:

We seek a Research Engineer for a new three-year project to develop an AI-powered physics-informed hybrid climate model trained on historical data that is capable of multidecadal forecasts that are more accurate than the current state of the art. We’re looking for someone passionate about improving the quality, efficiency and accessibility of climate models with AI and modern software engineering.  

This position is supported by a grant with a current period of performance ending September 22, 2029. Continuation of the position beyond that date depends on renewal, extension, or identification of additional funding. Ai2 will make reasonable efforts to notify the employee if funding for the position is expected to end.

Who We Are: 

Ai2’s Climate Modeling team is an international pioneer in using machine learning (ML) methodologies to improve on current physics-based climate models.  Our fast, accurate autoregressive open-source emulator, ACE, stably reproduces the climate and weather extremes of an existing climate model or a reanalysis. We have coupled ACE to a ML ocean model, trained it to  realistically account  for changing CO2 concentrations, and downscaled (super-resolved) ACE outputs from their 100 km native scale to to km-scale detail. Our tight-knit team of scientists and software engineers works closely with leading physics-based climate modeling centers to obtain unique training and testing data and get expert feedback on our progress. We collaborate with other leading research groups doing related AI work.

Your Next Challenge:

Physically-based climate models discretize equations representing individual time-evolving processes like clouds, rain, wind, land and sea-ice, and ocean currents on a computational grid.  Some processes (e.g. clouds) are less reliably encoded than others (e.g. winds).  Hence such climate models have biases in representing present-day climate and produce an undesirably large range of projections of future climate change for a given human-caused change in CO2 or other climate forcings.  Current AI-based climate models reduce present-day climate bias, but don’t generalize well to future climate change.