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 a Research Scientist in the Data team, your primary responsibility is to curate high quality data at the web-scale to fuel the development of next generation foundation models. You will work on exploring andconsolidatingdata sources and collaborate with cross-functional teams to conduct in-depth data research, contributing to MBZUAI’s mission of driving impactful AI discoveries and positioning the institution as a leader in the global AI research community. Your expertise will be key in enhancing the performance of large-scale machine learning models, while supporting the development of transformative AI tools that can influence industries worldwide. Key Responsibilities Pioneer web-scale data collection and curation methodologies for LLMs and multi-modal foundation models. Design and implement novel data synthesis pipelines for code, mathematics, and agentic reasoning datasets. Trace the impact of data from pre-training to final model capabilities and create automated quality assessment frameworks for massive datasets Design data recipes that maximize model capabilities across diverse domains. Optimize data-model co-design for improved training dynamics. Contribute to research papers and represent MBZUAI at industry conferences and events, showcasing the institution’s AI research and innovation. Academic Qualifications Minimum: Master’s in Computer Science, Data Science, or a related technical field, or equivalent practical experience required. Preferred: PhD or equivalent research experience in Machine Learning, NLP, or Data Science with a focus on LLMs and data is preferred. Professional Experience Experience working with large language models, including evaluation, fine-tuning, and prompt engineering. Strong Python development skills with a focus on research-grade code and scalable data pipelines. Familiarity with collecting and processing large-scale datasets from open-source and web resources. Demonstrated ability to work with ML infrastructure (e.g., model evaluation, optimization, debugging). Proactive mindset with the ability to identify impactful research questions and execute on them with minimal supervision. Effective communication and collaboration skills for working in cross-functional teams. Preferred Prior research experience in areas such as web data curation and mixing, synthetizing complex datasets for training, LLM evaluation, post-training data, efficient inference, LLM-as-a-judge, tokenization. Strong publication record in leading AI conferences (e.g., NeurIPS, ICLR, ICML, EMNLP) and/or prior contributions to open-source AI research or data tools. Hands-on experience training language/mutli-modal models from scratch. Benefits Include *Comprehensive medical, dental, and vision benefits *Bonus *401K Plan *Generous paid time off, sick leave and holidays *Paid Parental Leave *Employee Assistance Program *Life insurance and disability

Requirements

Must-haves

About the Institute of Foundation Models
Pioneer web-scale data collection and curation methodologies for LLMs and multi-modal foundation models.
Design and implement novel data synthesis pipelines for code, mathematics, and agentic reasoning datasets.
Trace the impact of data from pre-training to final model capabilities and create automated quality assessment frameworks for massive datasets
Design data recipes that maximize model capabilities across diverse domains.
Optimize data-model co-design for improved training dynamics.
Contribute to research papers and represent MBZUAI at industry conferences and events, showcasing the institution’s AI research and innovation.
Academic Qualifications
Minimum: Master’s in Computer Science, Data Science, or a related technical field, or equivalent practical experience required.
Professional Experience
Experience working with large language models, including evaluation, fine-tuning, and prompt engineering.
Strong Python development skills with a focus on research-grade code and scalable data pipelines.
Familiarity with collecting and processing large-scale datasets from open-source and web resources.
Demonstrated ability to work with ML infrastructure (e.g., model evaluation, optimization, debugging).
Proactive mindset with the ability to identify impactful research questions and execute on them with minimal supervision.
Effective communication and collaboration skills for working in cross-functional teams.

Nice-to-haves

Preferred: PhD or equivalent research experience in Machine Learning, NLP, or Data Science with a focus on LLMs and data is preferred.
Prior research experience in areas such as web data curation and mixing, synthetizing complex datasets for training, LLM evaluation, post-training data, efficient inference, LLM-as-a-judge, tokenization.
Strong publication record in leading AI conferences (e.g., NeurIPS, ICLR, ICML, EMNLP) and/or prior contributions to open-source AI research or data tools.
Hands-on experience training language/mutli-modal models from scratch.
Benefits Include

Application prep

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Keywords to mirror

aimachine learningpythonmlllm

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Paid Time Off

Parental Leave