Research Engineer – Statistics & Machine Learning | MBZUAI

Job Description

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) invites applications for a fully funded Research Engineer – Statistics and Machine Learning 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 in Abu Dhabi, UAE.

The successful candidate will support research projects in modern statistics, machine learning, and related areas. Projects may involve probabilistic machine learning, uncertainty quantification, conformal prediction, time-series forecasting, and anomaly detection, while candidates with relevant experience in other areas of Machine Learning are also encouraged to apply.

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

Statistics Machine Learning Research Engineer – Position Overview

The Research Engineer will support a range of research projects across Statistics, Machine Learning, Artificial Intelligence, and Data Science.

Key areas may include:

• Probabilistic prediction.
• Uncertainty quantification.
• Conformal prediction.
• Time-series forecasting.
• Anomaly detection.
• Statistical modelling.
• Machine Learning.
• Research software development.

The role combines model implementation and evaluation with reproducible research workflows and software-engineering practices.

Machine Learning and Statistical Methods

The successful candidate will:

• Implement Machine Learning methods.
• Implement statistical methods.
• Evaluate models using relevant datasets.
• Compare model performance.
• Establish appropriate baselines.
• Analyse results.
• Troubleshoot technical issues.

The position requires both strong quantitative foundations and practical research-engineering skills.

Probabilistic Prediction

Projects may include work on:

• Probabilistic prediction.
• Predictive uncertainty.
• Statistical learning.
• Model calibration.

These areas support research into reliable and uncertainty-aware predictive systems.

Uncertainty Quantification

The position may involve developing and evaluating methods for:

• Quantifying predictive uncertainty.
• Analysing uncertainty in model outputs.
• Supporting robust statistical and Machine Learning systems.

Conformal Prediction

Relevant projects may include conformal prediction, with applications to statistically principled predictive inference and uncertainty estimation.

Time-Series Forecasting

The Research Engineer may contribute to:

• Temporal modelling.
• Forecasting.
• Predictive analytics.
• Evaluation of time-series models.

Anomaly Detection

Projects may also include:

• Detecting unusual patterns.
• Identifying outliers.
• Developing statistical or Machine Learning methods for anomaly detection.

Data Processing and Reproducible Workflows

A major part of the role involves building reliable and reproducible workflows for:

• Data preprocessing.
• Model training.
• Model evaluation.
• Visualisation.
• Experimental tracking.

The successful candidate should be able to structure research workflows clearly and efficiently.

Research Experiments

Responsibilities include:

• Running experiments.
• Establishing baselines.
• Evaluating performance.
• Analysing model behaviour.
• Troubleshooting technical problems.
• Communicating findings to researchers.

Research Software Development

The Research Engineer will develop maintainable research software using practices such as:

• Version control.
• Code review.
• Automated testing.
• Documentation.
• Reproducible environments.

The role therefore requires attention to both scientific quality and software reliability.

Shared Research Code and Computing Workflows

The successful candidate will:

• Maintain shared research code.
• Support research computing workflows.
• Help enable efficient experimentation.
• Contribute to shared technical resources.

Academic and Industry Collaboration

The research group collaborates with leading academic and industry partners.

The Research Engineer will work with:

• Research-group members.
• Academic partners.
• Industry partners.

The successful candidate may also contribute to:

• Technical reports.
• Research papers.
• Demonstrations.

Python Programming

Strong programming skills in:

• Python

are required.

Required Qualification

Applicants must hold a:

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

Required Knowledge and Experience

Applicants should demonstrate:

• Knowledge of Machine Learning or statistical modelling.
• Experience implementing and evaluating models.
• Experience working with relevant datasets.
• Experience with Git.
• Experience with Linux.
• Experience with testing.
• Ability to write clear, maintainable, and documented research code.
• Excellent organisational skills.
• Excellent written English.
• Excellent spoken English.

Preferred Machine Learning Frameworks

Experience with commonly used Machine Learning frameworks or libraries is preferred, including:

• PyTorch.
• JAX.
• TensorFlow.
• scikit-learn.

Preferred Computing Experience

Preferred experience includes:

• Research software.
• Large-scale experimentation.
• GPU computing.
• High-performance computing.
• Cloud computing.

AI-Assisted Software Development

Familiarity with modern:

• AI-assisted software-development tools.
• Coding agents.

is preferred.

Research and Open-Source Experience

Relevant experience may include:

• Publications.
• Open-source contributions.
• Substantial thesis work.
• Applied projects in AI or related fields.

Multidisciplinary and International Collaboration

Experience working in:

• Multidisciplinary teams.
• International research collaborations.

is preferred.

Relevant Training

Relevant coursework or training may include:

• Machine Learning.
• Statistical modelling.
• Data Science.
• Software engineering.
• Related quantitative or computational areas.

Compensation and Benefits

The fully funded position includes:

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

Why Join MBZUAI?

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

The University offers opportunities for:

• Collaboration with leading AI researchers.
• Academic partnerships.
• Industry engagement.
• Government collaboration.
• Research and professional development.

Recommended Professional Development

🎓 Recommended Course: Bayesian Statistics Specialization

The Bayesian Statistics Specialization from the University of California, Santa Cruz is highly relevant to probabilistic prediction, uncertainty quantification, statistical modelling, and modern Machine Learning research.

It may be particularly useful to research engineers, statisticians, Machine Learning practitioners, data scientists, graduate researchers, and professionals working on uncertainty-aware predictive systems.

👉 Explore the Bayesian Statistics 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 Statistics and Machine Learning.
• Names and contact details for three 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 – Statistics and Machine Learning
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, or related discipline
Core Research Areas	Probabilistic prediction, uncertainty quantification, conformal prediction, time-series forecasting, anomaly detection
Programming Requirement	Strong Python skills
Software Engineering	Git, Linux, testing, documentation, maintainable research code
Preferred Frameworks	PyTorch, JAX, TensorFlow, scikit-learn
Preferred Computing Experience	GPU computing, HPC, cloud computing and large-scale experimentation
Preferred Research Experience	Publications, open-source contributions, thesis or applied AI projects
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, three referees
Application Deadline	Not stated