--- title: 'AI Engineer – LLM Data at Institute of Foundation Models' canonical: 'https://feeny.ai/job/ai-engineer-llm-data-institute-of-foundation-models-abu-dhabi-29cx2k7c8xhm' type: 'job' last_seen: '2026-10-26' --- # AI Engineer – LLM Data at Institute of Foundation Models - **Company:** Institute of Foundation Models - **Location:** Abu Dhabi, United Arab Emirates - **Employment:** full-time - **Work type:** onsite - **Posted:** 2025-06-17 - **Last confirmed live:** 2026-10-26 - **Apply:** https://jobs.lever.co/ifm-us/b6c7bc76-7a13-4a2c-84ce-d01c61d08d80 ## Job description ## About the Institute of Foundation Models We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy. As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.The Role As an AI Engineer specializing in LLM data, you will build and improve high-quality training data for foundation models across pre-training, mid-training, and post-training. Your work will include large-scale data curation and processing, LLM-based data synthesis, data quality evaluation, and experimentation to understand how different data choices affect model performance. ## Key Responsibilities - Build, curate, and improve large-scale datasets for LLM pre-training, mid-training, and post-training. - Rapidly support time-sensitive data and model development tasks in a fast-moving research environment. - Develop and improve data processing pipelines including data extraction, cleaning, filtering, deduplication, quality scoring, transformation, and dataset composition. - Design and implement LLM-based data synthesis and augmentation pipelines, including prompt-based generation, filtering, refinement, and quality control of synthetic data. - Research and apply methods for improving training data quality, diversity, coverage, and efficiency. - Design experiments to understand the relationship between training data and model performance, and use model evaluation results to guide data improvements. - Develop scalable tools and workflows for processing and analyzing large datasets efficiently. - Analyze datasets using both statistical and model-based methods to identify quality issues, biases, duplication, distributional gaps, and opportunities for improvement. - Collaborate closely with researchers, model engineers, and other data teams to translate model development needs into effective data solutions. - Document datasets, experiments, data processing methodologies, and key findings clearly to support reproducibility and knowledge sharing. Professional Experience - Required - Strong programming skills in Python and experience building reliable engineering or research workflows. - Hands-on experience with machine learning, deep learning, NLP, or large language models. - Good understanding of modern LLM development, including how training data is used in pre-training and/or post-training. - Experience working with large-scale datasets, including data processing, analysis, filtering, transformation, and quality control. - Ability to independently investigate data or model quality issues, design experiments, and make data-driven technical decisions. - Familiarity with common machine learning and LLM tools and frameworks such as PyTorch, Hugging Face, vLLM, or similar technologies. - Strong problem-solving skills and the ability to work effectively in a fast-paced AI research and engineering environment. - Strong communication and collaboration skills, with the ability to work closely with researchers and engineers across different technical areas. Professional Experience - Preferred - Experience preparing data for large-scale LLM pre-training, continued/mid-training, supervised fine-tuning, preference optimization, reinforcement learning, or other post-training workflows. - Experience with synthetic data generation using LLMs, including generation, filtering, verification, or quality evaluation. - Experience designing or running LLM evaluations, benchmarks, model training, or fine-tuning experiments. - Understanding of how data quality, mixture, diversity, and scaling affect foundation model performance. - Experience with large-scale or distributed data processing and compute infrastructure. - Experience working with research teams on rapidly evolving foundation model or generative AI projects. - Contributions to open-source AI/ML projects, relevant publications, or demonstrated hands-on work with modern foundation models are a plus. ## About Institute of Foundation Models ## Company Overview - **One-liner**: The Institute of Foundation Models (IFM) is a global AI research lab dedicated to the open and independent development of frontier-class foundation models, operating under the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). - **Entity Type**: Academic Research Institute (part of MBZUAI, a university) - **Headquarters**: Abu Dhabi, United Arab Emirates (with labs in Sunnyvale, CA, USA and Paris, France) - **Founded**: 2026 (launched with Silicon Valley lab) - **Founders**: Part of MBZUAI, led by President Eric Xing ## Core Business - **Primary industry**: Artificial Intelligence research, foundation model development, open-source AI - **Target customers**: Researchers, academia, industry partners, and the global AI community (B2B/Research/Public sector) - **Mission**: To produce the world’s leading models across modalities while ensuring responsible, open, and socially meaningful impact. ## Products & Services For each major offering: - **JAIS Series**: State-of-the-art open-source Arabic LLM covering Modern Standard Arabic and regional dialects, including Moroccan Arabic (Darija). Fully documented training lifecycle. - **Vicuna**: Lightweight open chatbot surpassing 90% of ChatGPT and Bard quality. - **K2 (K2-65B)**: Large language model with advanced reasoning capabilities, focusing on sustainable performance. Updated version soon to be released. - **PAN World Model**: Next-gen foundation model for embodied reasoning and physical-world simulation, integrating multimodal inputs (language, video, spatial data, physical actions). Includes PAN-Agent for multimodal reasoning tasks. - **FM For Bio – GET**: Domain-specific model for biology. - **LLM360**: Open-source framework enhancing transparency and collaboration in LLM research, providing training code, datasets, and model checkpoints. - **Research partnerships**: Collaboration with academia, startups, and enterprises via shared compute, co-publishing, and co-development. ## Market Standing - **Valuation/Market Cap**: Not applicable (non-commercial research institute) - **Key Metric**: Funded by MBZUAI; specific budget not publicly disclosed. Notable open-source model releases (JAIS, Vicuna, K2, PAN). - **Notable Investors/Partners**: MBZUAI, with international advisory boards and peer review processes. Partnerships with industry leaders, academic institutions, and public organizations. - **Growth Signals**: Launch of Silicon Valley lab in Sunnyvale, CA (2026); expansion into Paris and Abu Dhabi nodes; active hiring for research interns, distributed ML engineers, HPC engineers; release of flagship models (PAN, K2 update). ## Competitive Advantages - **Openness & transparency**: Full release of training code, datasets, and model checkpoints through LLM360 – one of the most transparent approaches in AI. - **Global research network**: Three hubs (Abu Dhabi, Silicon Valley, Paris) combining academic rigor with startup agility. - **Access to large-scale compute**: High-performance computing infrastructure for training frontier models. - **Multilingual & cultural focus**: JAIS addresses underrepresentation of Arabic and other languages, preserving cultural authenticity. - **World model innovation**: PAN differentiates from text-only models by predicting comprehensive world states for advanced reasoning and simulation. ## Strategic Focus - Continue building open, powerful foundation models across language, vision, multimodal, and domain-specific systems. - Expand global collaboration and talent acquisition, especially in Silicon Valley. - Advance responsible AI with safety systems and international advisory boards. - Drive real-world impact through partnerships in scientific discovery, human-AI interaction, and public good. ## Why Work Here - **Culture**: Collaborative, cutting-edge academic research environment with a mission to open-source AI for global benefit. Teams span Abu Dhabi, Paris, and Silicon Valley. - **Remote/hybrid/office**: All listed roles are **on-site** (Sunnyvale, CA or Abu Dhabi). No remote policy indicated. - **Notable perks**: Work with world-class researchers, access to large-scale compute, opportunity to publish and contribute to open-source models. The institute combines the agility of a startup with the resources of an established university. - **Current openings**: AI Research Internship (LLM), Distributed Machine Learning Engineer, Eval360 Error Analysis Engineer, High Performance Computing Software Engineer (Supercomputing) – all on-site in Sunnyvale or Abu Dhabi. ## Sources 1. [ifm.ai](https://ifm.ai/) 2. [ifm.ai/about](https://ifm.ai/about/) 3. [jobs.lever.co/ifm-us](https://jobs.lever.co/ifm-us) 4. [LinkedIn – Institute of Foundation Models](https://www.linkedin.com/company/institute-of-foundation-models) 5. 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