Research Engineer – AI for Weather Forecasting | MBZUAI

Job Description

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) invites applications for a fully funded Research Engineer – AI for Weather Forecasting position within its Computing and Mathematical Sciences Division.

The appointment is available for 3–12 months, with flexibility, under the supervision of Prof. Souhaib Ben Taieb.

The position forms part of a collaborative research programme between MBZUAI and the AI for Climate initiative at the University of Chicago, supported by funding from the International Affairs Office at the Presidential Court of the United Arab Emirates and the Gates Foundation.

The programme focuses on advancing AI-based weather forecasting and supporting its operational use in low- and middle-income countries.

Employer: Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
Division: Computing and Mathematical Sciences
Location: Abu Dhabi, United Arab Emirates
Position: Research Engineer – AI for Weather Forecasting
Appointment Duration: 3–12 months, flexible
Supervisor: Prof. Souhaib Ben Taieb
Open Date: 21 September 2026

AI Weather Forecasting Research Engineer – Position Overview

The Research Engineer will develop, evaluate, and support machine-learning approaches for weather forecasting and related spatiotemporal problems.

The role combines:

• Machine Learning.
• Artificial Intelligence.
• Weather forecasting.
• Climate data analysis.
• Spatiotemporal modelling.
• Scientific computing.
• Forecast evaluation.
• Research software development.
• Reproducible computational workflows.
• International research collaboration.

The position has a strong applied focus, including the development of forecasting systems suitable for both research and operational settings.

Machine Learning for Weather Forecasting

The successful candidate will implement and evaluate machine-learning methods for:

• Weather forecasting.
• Climate analysis.
• Spatiotemporal problems.
• Forecast modelling.
• Related environmental applications.

Applicants should have practical experience applying machine-learning methods to weather, climate, or spatial data.

Data Processing and Model Training

The Research Engineer will build reliable and reproducible workflows for:

• Processing data.
• Training models.
• Evaluating forecasts.
• Managing computational experiments.

The role requires careful attention to reproducibility, reliability, and technical quality.

Forecast Evaluation

Responsibilities include:

• Conducting experiments.
• Establishing appropriate baselines.
• Evaluating forecasting models.
• Analysing model performance.
• Troubleshooting technical issues.

The successful candidate should be able to assess model performance systematically and interpret results effectively.

Forecasting Models and Research Software

The position includes developing and documenting:

• Forecasting models.
• Research software.
• Computational workflows.
• Supporting technical documentation.

Research outputs should be suitable for use in both experimental research and operational environments.

Weather and Climate Data

Applicants should have experience working with weather or climate datasets such as:

• Reanalysis data.
• Satellite data.
• Observational data.
• Forecast data.

The ability to manage large scientific datasets is particularly relevant to this role.

Python and Machine Learning

Candidates must demonstrate:

• Strong Python programming skills.
• Experience with a commonly used machine-learning framework.
• Knowledge of Machine Learning.
• Knowledge of statistical modelling.

Scientific Data Tools

Familiarity with scientific-data tools and formats is preferred, including:

• xarray.
• Dask.
• NetCDF.
• Zarr.

These technologies are particularly relevant to large-scale weather and climate datasets.

High-Performance and Distributed Computing

Experience in advanced computational environments is preferred.

Relevant areas include:

• GPU computing.
• Distributed training.
• High-performance computing.
• Cloud computing.

Probabilistic Forecasting and Uncertainty

Preferred expertise also includes:

• Probabilistic forecasting.
• Ensemble methods.
• Uncertainty quantification.
• Data assimilation.
• Forecast evaluation.

These areas support the development of robust and operationally useful weather-prediction systems.

Research Engineering and Reproducibility

Relevant technical experience includes:

• Git.
• Linux.
• Automated testing.
• Reproducible environments.
• Container tools.

The Research Engineer should be able to develop software and workflows that can be reliably reproduced and maintained.

Environmental and Agricultural Applications

Familiarity with applications of weather forecasts in areas such as:

• Agriculture.
• Environmental analysis.

is preferred.

The wider research programme aims to support operational weather forecasting in low- and middle-income countries.

International Research Collaboration

The successful candidate will collaborate with:

• MBZUAI researchers.
• University of Chicago researchers.
• National partners.
• International partners.
• Weather and climate-service stakeholders.

Experience working in interdisciplinary or international collaborations is preferred.

Required Qualification

Applicants must hold a:

• Master’s degree in Computer Science, Applied Mathematics, Statistics, Data Science, Atmospheric or Climate Science, or a related discipline.

Required Experience and Knowledge

Applicants should demonstrate:

• Experience applying Machine Learning to weather forecasting, climate analysis, or spatial data.
• Strong Python programming skills.
• Experience with a commonly used Machine Learning framework.
• Experience working with weather or climate datasets.
• Knowledge of Machine Learning.
• Knowledge of statistical modelling.
• Excellent organisational skills.
• Excellent written English.
• Excellent spoken English.

Preferred Qualifications

Preferred experience includes:

• Scientific-data tools such as xarray, Dask, NetCDF, or Zarr.
• GPU computing.
• Distributed training.
• High-performance computing.
• Cloud computing.
• Probabilistic forecasting.
• Ensemble methods.
• Uncertainty quantification.
• Data assimilation.
• Forecast evaluation.
• Git.
• Linux.
• Automated testing.
• Reproducible environments.
• Container tools.
• Agricultural or environmental applications of weather forecasts.
• Interdisciplinary collaboration.
• International collaboration.

Relevant coursework or training may include:

• Machine Learning.
• Spatiotemporal modelling.
• Weather or climate science.
• Data assimilation.
• Scientific computing.

Compensation and Benefits

The fully funded position includes:

• Competitive tax-free monthly salary.
• Housing allowance.
• Comprehensive medical insurance.
• Annual return flight.
• Full visa application support.

Why Join MBZUAI?

MBZUAI offers an international research environment focused on advanced Artificial Intelligence research, innovation, and collaboration.

The University highlights opportunities for:

• Collaboration with leading AI researchers.
• Industry partnerships.
• Government collaboration.
• Advanced AI research.
• Interdisciplinary research.
• Professional development.

Recommended Professional Development

🎓 Recommended Course: Machine Learning Specialization

The Machine Learning Specialization from DeepLearning.AI and Stanford Online provides structured training in core machine-learning methods relevant to predictive modelling, model evaluation, data-driven forecasting, and AI research.

It may be particularly relevant to research engineers, data scientists, climate and weather researchers, graduate students, and professionals applying Machine Learning to forecasting and scientific data.

👉 Explore the Machine Learning Specialization on Coursera

Affiliate disclosure: Arab Academic Jobs may receive a commission if you enroll through this link, at no additional cost to you.

Application Requirements

Interested candidates should submit:

• Cover Letter.
• Curriculum Vitae (CV).
• Link to Google Scholar profile.
• A 1–2 page research statement outlining directions the candidate would like to pursue in AI and weather forecasting.
• Names and contact details for two referees.

Referees will be contacted if the candidate progresses to the final stages of the recruitment process.

Job Summary

Employer	Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
Division	Computing and Mathematical Sciences
Location	Abu Dhabi, UAE
Position	Research Engineer – AI for Weather Forecasting
Appointment Duration	3–12 months, flexible
Supervisor	Prof. Souhaib Ben Taieb
Open Date	21 September 2026
Required Qualification	Master’s in Computer Science, Applied Mathematics, Statistics, Data Science, Atmospheric or Climate Science, or related discipline
Core Research Areas	AI, Machine Learning, weather forecasting, climate analysis and spatiotemporal modelling
Programming Requirement	Strong Python skills and experience with a commonly used ML framework
Data Experience	Reanalysis, satellite, observational or forecast weather/climate data
Preferred Tools	xarray, Dask, NetCDF, Zarr, Git, Linux and container tools
Preferred Computing Experience	GPU computing, distributed training, HPC or cloud computing
Preferred Forecasting Expertise	Probabilistic forecasting, ensembles, uncertainty quantification, data assimilation and forecast evaluation
Collaboration	MBZUAI, University of Chicago and national/international weather and climate partners
Benefits	Tax-free salary, housing allowance, medical insurance, annual return flight and visa support
Application Materials	Cover letter, CV, Google Scholar profile, 1–2 page research statement and two referees
Application Deadline	Not stated