Posted May 26, 2026
Follow the latest research related to LLMs and data quality in particular. Be familiar with the most relevant open-source datasets and models. - Design and implement complex pipelines that can generate large amounts of data while maintaining high diversity and optimizing the resources available. - Closely work with other teams such as Pretraining, Posttraining, Evals and Product to ensure short feedback loops on the quality of the models delivered. - Suggest, conduct and analyze data ablations or training experiments that aim to improve the quality of the datasets generated via quantitative insights. ## SKILLS & EXPERIENCE
Strong machine learning and engineering background
Experience with Large Language Models (LLM), including:
Understanding of transformer architectures and how LLMs learn
Data ablations and scaling laws
Mid-training and Post-training techniques
Training reasoning and agentic models
Experience with evals tracking model capabilities (general knowledge, reasoning, math, coding, long-context, etc)
Experience in building trillion-scale pretraining datasets, and familiarity with concepts like data curation, deduplication, data mixing, tokenization, curriculum, impact of data repetition, etc. - Excellent programming skills in Python
Strong prompt engineering skills
Experience working with large-scale GPU clusters and distributed data pipelines
Strong obsession with data quality
Research experience:
Author of scientific papers on any of the topics: applied deep learning, LLMs, source code generation, etc. - is a nice to have
Can freely discuss the latest papers and descend to fine details
Is reasonably opinionated
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