Research Engineer, Climate Modeling

The Allen Institute for Artificial Intelligence · Seattle, WA

  • Junior
  • Full-time
  • $146,880 – $220,320
  • Posted 2026-09-25
  • Confirmed live on 30 September 2026

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Job description

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.

We are starting a 3-year project funded by google.org to develop an open source ‘AI-first’ climate model that can demonstrably project future climates more accurately than current physically-based climate models when trained on historical observations by judicious design of the AI to incorporate physical principles that reliably apply in any climate, seen or unseen. The model should be lightweight and easy for an ML-savvy climate scientist to train and deploy. Our starting point is the Google Research NeuralGCM model, a hybrid ML model that uses a conventional discretization of winds encoded in JAX and learns a column-local representation of all other atmospheric and land processes from historical data. Compared to other AI climate models, NeuralGCM uses ML in a conceptually simpler way and shows somewhat better future-climate generalization skill. It is an open-weight model but its training pipeline and documentation need to be adapted for a broader user community.

You will join a small team to develop the desired NeuralGCM+ model and foster its uptake by the climate modeling community. This includes coupling it to an ML-based ocean model and designing approaches to demonstrably improve its skill in unseen climates. This position has a three year term; there is potential for longer-term funding if the NeuralGCM+ project demonstrates exceptional progress.

You'll get to work on:

• Adapting the training pipeline and data needs of NeuralGCM to run efficiently on Google Cloud resources and on NVIDIA GPUs. Note that NeuralGCM is written in JAX and implemented on TPUs.

• Documenting this training pipeline and distributing code on a GitHub repo.

• Implementing and testing a strategy for coupling NeuralGCM with the chosen ML ocean model.

• Open-source code development as part of a tightly connected team, including daily meetings, code review, design documents and external collaborators.

• Develop and present tutorials on NeuralGCM+ to interested user groups.

What You’ll Need:

• 2-4 years of experience building and deploying a relevant ML application in a practical or academic research setting and be fluent in Python and collaborative coding practices.

• A B.Sc. in computer or computational science, atmospheric science or a related geophysical science, or applied mathematics/statistics.

• Experience in developing ML training code for use in a cloud

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