Senior AI Researcher

We're Hiring: Senior AI Researcher

Location: United Arab Emirates (Remote)

Employment Type: Full-Time

Experience Level: Senior

Work Arrangement: Fully Remote

About Us

We are a globally focused organization committed to advancing artificial intelligence research, intelligent technologies, and data-driven solutions that address complex business and operational challenges across diverse markets.

Our multidisciplinary teams collaborate across Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Product, Research, Technology, Robotics, and Operations to translate advanced research into practical, scalable, and measurable applications.

The Role

We are seeking an experienced Senior AI Researcher to lead advanced research in artificial intelligence, machine learning, deep learning, generative AI, and emerging intelligent systems.

The ideal candidate will combine strong theoretical foundations with practical research and engineering capabilities to investigate new methods, develop novel models and algorithms, conduct rigorous experiments, publish or document research findings, and translate promising research into production-oriented technologies.

Key Responsibilities

• Lead advanced AI and machine-learning research projects from problem formulation through experimentation, validation, and technical delivery.
• Identify high-value research opportunities aligned with strategic, technological, and business priorities.
• Define research questions, hypotheses, methodologies, evaluation frameworks, and measurable objectives.
• Conduct literature reviews and monitor developments in artificial intelligence, machine learning, deep learning, and related fields.
• Analyze academic papers, technical publications, benchmarks, patents, open-source projects, and emerging research directions.
• Develop novel algorithms, models, architectures, and learning methodologies.
• Research improvements in model accuracy, efficiency, robustness, generalization, scalability, and interpretability.
• Investigate foundation models, large language models, multimodal AI, generative AI, reinforcement learning, computer vision, NLP, and other relevant AI areas.
• Design and conduct controlled experiments to evaluate new AI methods and hypotheses.
• Develop experimental protocols that support reproducible and statistically rigorous research.
• Build research prototypes and proof-of-concept systems to validate new concepts.
• Implement and test machine-learning algorithms using appropriate programming languages and frameworks.
• Develop training, validation, and testing pipelines for advanced AI models.
• Prepare and curate datasets for research and experimentation.
• Investigate data quality, distribution shifts, bias, imbalance, missing information, and other factors affecting model performance.
• Develop data augmentation, synthetic-data generation, sampling, and preprocessing strategies.
• Conduct model training, fine-tuning, transfer learning, and parameter optimization.
• Explore efficient training approaches including parameter-efficient fine-tuning, distillation, quantization, pruning, and other optimization methods.
• Develop and evaluate novel architectures and model components.
• Benchmark new approaches against established baselines and state-of-the-art methods.
• Design evaluation metrics and datasets appropriate to specific research problems.
• Perform ablation studies to understand the contribution of individual model components, features, datasets, and training techniques.
• Conduct statistical analysis of experimental results and assess the significance and reliability of findings.
• Perform robustness testing, sensitivity analysis, stress testing, and out-of-distribution evaluation.
• Investigate model failure modes, edge cases, hallucinations, biases, vulnerabilities, and unexpected behaviors.
• Develop methods to improve model reliability, interpretability, safety, and controllability.
• Research techniques for responsible AI, fairness, transparency, privacy, security, and model governance.
• Explore methods for evaluating large language models and generative AI systems, including factuality, reasoning, instruction following, groundedness, and safety.
• Research retrieval-augmented generation, agentic AI, tool use, knowledge integration, and intelligent decision-making systems.
• Investigate multimodal learning involving text, images, audio, video, structured data, and other information modalities.
• Research reinforcement-learning and optimization techniques for intelligent systems where appropriate.
• Explore techniques for efficient inference, model compression, hardware acceleration, and large-scale AI deployment.
• Develop scalable experimentation infrastructure in collaboration with Machine Learning and Software Engineering teams.
• Maintain reproducible research environments, experiment tracking, model repositories, datasets, and technical documentation.
• Develop automated experimentation, evaluation, benchmarking, and model-analysis workflows.
• Collaborate with engineering teams to transition validated research concepts into production systems.
• Assess the feasibility, scalability, computational requirements, and operational implications of research outcomes.
• Conduct technical feasibility studies for emerging AI technologies and research directions.
• Evaluate open-source and commercial AI models, platforms, frameworks, datasets, and infrastructure.
• Develop internal benchmarks and research datasets to measure AI-system performance.
• Establish research standards covering experimentation, reproducibility, documentation, evaluation, and data governance.
• Prepare technical reports, research papers, white papers, patents, internal research notes, and executive briefings.
• Present research findings to senior leadership, technical teams, research partners, and external audiences where appropriate.
• Contribute to academic publications, conferences, workshops, technical communities, or industry research initiatives where appropriate.
• Establish relationships with universities, research laboratories, technology companies, and external research organizations.
• Manage research collaborations, specialist consultants, external researchers, and technology partners where required.
• Mentor AI researchers, machine-learning engineers, data scientists, and junior technical professionals.
• Review research methodologies, experimental designs, technical papers, and model-development approaches.
• Provide technical leadership on complex AI research challenges.
• Identify opportunities to combine multiple AI techniques to address complex business and scientific problems.
• Monitor emerging AI capabilities and assess their potential practical applications.
• Develop research roadmaps, project plans, milestones, resource requirements, and technical priorities.
• Manage multiple research initiatives while maintaining scientific rigor and reproducibility.
• Provide leadership with regular updates on research progress, experimental results, technical risks, emerging technologies, and commercialization opportunities.

Key Performance Indicators

• Research project delivery
• Research milestone completion
• Experimental cycle time
• Research hypothesis validation
• Model performance improvement
• Benchmark performance
• State-of-the-art comparison
• Experiment reproducibility
• Experimental success rate
• Research prototype completion
• Prototype-to-production conversion
• Model accuracy and generalization
• Model robustness
• Out-of-distribution performance
• Inference efficiency
• Training efficiency
• Computational cost optimization
• Dataset quality
• Evaluation coverage
• Benchmark development
• Ablation-study completion
• Model failure-rate reduction
• AI safety evaluation coverage
• Responsible-AI compliance
• Research publication output
• Patent and intellectual-property contribution
• Technical documentation quality
• Research adoption by engineering teams
• Research-to-product conversion
• Stakeholder satisfaction
• External research collaboration
• Technology evaluation completion
• Research roadmap execution
• Knowledge-sharing contribution
• Mentoring and team development
• Research infrastructure improvement
• Experiment automation
• Model monitoring and evaluation improvement
• Innovation contribution
• Strategic research impact

Ideal Candidate

The successful candidate should have strong experience in artificial intelligence, machine learning, deep learning, computer science, computational research, generative AI, or a closely related research discipline, preferably within a technology company, research laboratory, university, advanced engineering organization, AI startup, or innovation-focused environment.

The candidate should demonstrate:

• Deep understanding of artificial intelligence and modern machine-learning theory.
• Proven experience conducting original AI or machine-learning research.
• Strong knowledge of deep-learning architectures, optimization, representation learning, and statistical learning.
• Experience researching and developing advanced AI models and algorithms.
• Strong programming skills in Python and experience with modern AI frameworks such as PyTorch, TensorFlow, JAX, or equivalent technologies.
• Experience designing rigorous experiments and evaluating research hypotheses.
• Strong understanding of statistical analysis, experimental design, and model evaluation.
• Experience working with large-scale datasets and computationally intensive research environments.
• Strong knowledge of model training, fine-tuning, validation, benchmarking, and optimization.
• Experience with one or more advanced AI domains such as large language models, generative AI, computer vision, NLP, reinforcement learning, multimodal AI, or autonomous systems.
• Strong ability to analyze academic literature and identify meaningful research opportunities.
• Experience reproducing, extending, or improving published research.