AI Research Engineer

About the Role

Join teams at Meta working at the intersection of frontier AI and real-world product impact, embedded within the Facebook ecosystem to reimagine core experiences and reshape how people discover content, connect with creators, and interact. The work spans applied GenAI, including:

• Building post-training, evaluation, and serving systems for frontier LLMs into reliable, high-quality product experiences used by billions.
• Developing a general-purpose agentic platform to power a wide range of GenAI products across Facebook, enabling faster, safer, and scaled shipping.
• Creating systems for capacity and cost optimizations through model fine-tuning, post-training, and other techniques.
• Adapting and scaling these systems across Meta's products.

Key Responsibilities

• Contribute to the training of next-generation multimodal foundation models, advancing their capabilities in understanding, generation, and grounding, and enabling them for downstream product use-cases.
• Support creative data sourcing, high-quality pre/mid/post-training data curation, and scale and optimize data pipelines for multimodal large language models (LLMs).
• Lead, collaborate, and execute on research that pushes the state of the art in multimodal reasoning and generation research, prioritizing research directly applicable to Meta’s product development.

Requirements & Tech Stack

• Work could span post-training pipelines (SFT, RLHF, synthetic data generation), evaluation methodology (auto-judge design, benchmark construction, human-AI calibration), production serving systems (RAG, memory, multi-modal generation), multi-agent orchestration, or E2E experience of building agentic products, all grounded in shipping to real users at scale.
• Experience working in AI/ML environments.
• Ability to manage data pipelines and versioning.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
• Experience as a formal technical lead, leading major technical initiatives with XFN impact, and/or influencing strategy across multiple teams.
• Impressive engineering background.
• PhD in ML not required.