Research Engineer, Pre-training Data - MSL FAIR
Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Nov 27, 2025. Meta scores B on the Alion truth index.
Our team contributes to data curation across all stages of LLM development (pre-training, mid-training, post-training) and all domains/modalities (e.g., web, code, agent, multilingual). We tackle the hardest challenges at trillion-scale, including organic data curation, synthetic data generation, agent and interaction data, and frontier paradigms that redefine what's possible.
Based in Meta Superintelligence Labs (MSL) within the Fundamental AI Research Organization (FAIR), you'll directly contribute to Meta’s frontier models like Llama, while having the chance to collaborate with researchers and engineers across MSL.
Responsibilities
Collaborate with cross-functional teams to develop Meta’s next foundational models Architect efficient and scalable data curation systems and pipelines Fundamentally improve our data velocity across workflows and projects by contributing to the advancement of data tooling Execute on high priority projects in pre-training, mid-training, or post-training data curation Apply specialized expertise in agentic data, synthetic data, reasoning data, web parser, coding data, data scaling laws, or datamix optimization Lead complex technical projects end-to-end
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 2+ years of industry research experience in LLM/NLP or related AI/ML models Experience as a formal technical lead, leading major technical initiatives with cross-functional impact, and/or influencing strategy across multiple teams Practical experience with pre-training or mid-training data curation for large foundational models and experience working with organic, synthetic, agentic, or reasoning data for LLMs Demonstrated data infrastructure and software background, and experience building data tooling and services Published research in leading peer-reviewed conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) and/or demonstrated significant industry influence in the field of AI Experience working on frontier-quality/state-of-the-art Large Language Models Masters degree or PhD in Computer Science or a related technical field Hands-on experience with modeling frameworks like PyTorch Hands-on experience on SQL and large-scale data handling, with familiarity of frameworks like Spark and Hive