MBZUAI Industry Collaboration
Career Opportunities
May 2025
MBZUAI, Abu Dhabi
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) is the world’s first graduate‑level, research‑focused AI university. Since its founding in 2020 it has grown to 80+ world‑class faculty and 330+ graduate students and is already ranked top‑25 globally in AI‑related fields (CSRankings). Located in Abu Dhabi, MBZUAI offers a vibrant, well‑funded environment that supports cutting‑edge research, industry collaboration, and career‑boosting opportunities.
Conversational AI & NLP
The Postdoctoral Research Scientist in Conversational AI & NLP will lead the research and development of the core dialogue engine. This involves designing, implementing, and evaluating advanced NLP models to ensure coherent, engaging, and safe user interactions. The role requires a deep understanding of large language models (LLMs), dialogue systems, context management, and techniques to mitigate risks like hallucination and manipulation.
Key Responsibilities
- - Design and develop the core conversational capabilities of the booking agent using state-of-the-art LLMs and NLP techniques.
- - Investigate and implement strategies to prevent misleading advice and enhance model safety and robustness against manipulation.
- - Collaborate closely with the project team to define data requirements for training and fine-tuning conversational models.
- - Coordinate the evaluation of the agent's conversational quality, safety, and security.
- - Work with engineers to integrate the conversational module with backend systems and user interfaces.
- - Stay updated on the latest research in conversational AI, dialogue systems, LLM safety, and related fields.
- - Publish research findings in top-tier conferences and journals.
- - Mentor junior researchers or research assistants (RAs) involved in related tasks.
- - Collect, preprocess, and store conversational data, ensuring compliance with privacy standards and efficient use in model training and improvement.
- - Benchmark and evaluate NLP models to assess performance across various metrics and fine-tune models based on results.
Academic Qualifications
- - PhD in Computer Science, Machine Learning, or a related field with a focus on Natural Language Processing and Conversational AI.
Professional Qualifications
Minimum
- - Experience in developing and evaluating dialogue systems or conversational agents.
- - Experience in managing, preprocessing, and storing data while ensuring privacy compliance.
- - Proficiency in fine-tuning, benchmarking, and evaluating NLP models.
- - Strong understanding of Large Language Models (LLMs), including their strengths and limitations.
- - Knowledge of methods for evaluating conversational AI systems (e.g., coherence, relevance, safety).
- - Proficiency in working with deep learning frameworks.
- - Track record of relevant publications in leading ML/NLP conferences or journals.
- - Excellent communication and collaboration skills.
Preferred
- - Experience with LLM safety, alignment, and hallucination-mitigation techniques.
- - Experience with bilingual or multilingual NLP models.
- - Familiarity with evaluation methods for conversational interfaces.
- - Experience working in collaborative, multi-disciplinary research projects.
Personalization & Recommendation
The Postdoctoral Research Scientist in Recommendation & Personalization will focus on finding optimal offers based on user behaviour. This involves leveraging user interaction history, building user profiles, integrating external knowledge sources, and working with techniques like knowledge graphs, Retrieval‑Augmented Generation (RAG), grounding information for accuracy, and exploring the use of causal reasoning to offer timely and relevant suggestions that enhance user value and align with business objectives.
Key Responsibilities
- - Design and implement algorithms for personalized product/service recommendations and upselling within a conversational context.
- - Explore and utilize techniques like Knowledge Graphs and Retrieval‑Augmented Generation (RAG) to ground recommendations and provide contextually relevant information (e.g., visa requirements, local attractions).
- - Investigate the use of counter‑factual reasoning or causality frameworks to understand why certain recommendations are effective and to improve strategy.
- - Design an infrastructure to continually learn from new data to improve recommendations made to the user.
- - Integrate recommendations with the NLP engine, mitigate hallucinations and ensure the information provided is accurate.
- - Work with data scientists and engineers to integrate recommendation modules with user data sources and backend systems (e.g., pricing engines, loyalty programs).
- - Mentor junior researchers or research assistants (RAs) involved in related tasks.
- - Design and conduct experiments to evaluate the effectiveness of different recommendation and upselling strategies.
- - Publish research findings in top‑tier conferences and journals.
Academic Qualifications
- - PhD in Computer Science, Machine Learning, or a related field with a focus on Recommender Systems, Personalization, Information Retrieval, or Data Mining.
Professional Qualifications
Minimum
- - Experience in developing and evaluating dialogue systems or conversational agents.
- - Strong understanding of machine learning techniques relevant to personalization and user modelling.
- - Hands‑on experience with Retrieval‑Augmented Generation (RAG) models, including integrating external knowledge sources to improve model performance.
- - Experience in developing and evaluating recommendation systems.
- - Familiarity with techniques like collaborative filtering, content‑based filtering, sequence modeling, and reinforcement learning for recommendations.
- - Proficiency in working with deep learning frameworks.
- - Expert knowledge of the latest research on recommender systems and personalization.
- - Track record of relevant publications in leading NLP/ML conferences or journals.
- - Excellent communication and collaboration skills.
Preferred
- - Experience with applying recommendations in conversational AI or chatbot settings.
- - Knowledge of causal inference or counter‑factual reasoning techniques.
- - Experience with A/B testing and online evaluation of recommendation systems.
- - Familiarity with economic principles related to pricing and upselling.
- - Experience working in collaborative, multi‑disciplinary research projects.
Affective & Persuasive AI
The Postdoctoral Research Scientist in Persuasive Language Generation will lead the development of advanced dialogue systems, focusing on methods to seamlessly integrate recommendations into conversations that are sensitive to the user’s captured intent and do not degrade conversational quality. The role involves eliciting user intent, adapting to user styles, and setting up an infrastructure to collect data to continually improve the quality of persuasive language generated by the agent.
Key Responsibilities
- - Apply NLP/ML techniques to recognize user intent across text and potentially voice inputs, optimizing conversation flows to adapt dynamically to the user’s style and ensure smooth transitions and task completion.
- - Research and implement methods for maintaining conversational coherence and effectively embedding past discussion context to interpret multi‑turn interactions.
- - Implement NLP models that elicit user preferences and recognize emotions (e.g., frustration, satisfaction), enabling the agent to adjust its tone and responses accordingly.
- - Use user‑centered design methods (such as A/B testing, user interviews, and usability studies) to evaluate and improve the effectiveness of the agent’s conversational interface.
- - Work with data scientists and engineers to integrate the conversational module with backend systems and user interfaces.
- - Mentor junior researchers or research assistants (RAs) involved in related tasks.
- - Stay updated on the latest research in conversational AI and dialogue systems.
- - Publish research findings in top‑tier conferences and journals.
Academic Qualifications
- - PhD in Computer Science, Machine Learning, or a related field with a focus on Natural Language Processing and Conversational AI.
Professional Qualifications
Minimum
- - Experience in developing and evaluating dialogue systems or conversational agents.
- - Strong background in conversational AI, including experience with intent classification, contextual understanding, and multi‑turn dialogue systems.
- - Proficiency in working with deep learning frameworks.
- - Experience with emotion detection, sentiment analysis, and tailoring conversational tone based on user feedback or emotional state.
- - Strong analytical skills and experience working with user interaction data.
- - Excellent communication and collaboration skills.
- - Track record of relevant publications in leading NLP/ML conferences or journals.
Preferred
- - Experience with bilingual or multilingual language models.
- - Familiarity with evaluation methods for conversational interfaces.
- - Experience working in collaborative, multi‑disciplinary research projects.
