AI Research Engineer

About the Role

We’re hiring Research Engineers to join teams across Meta working at the intersection of frontier AI and real-world product impact. You’ll be embedded directly in Facebook’s ecosystem, helping reimagine core experiences and reshape how people discover content, connect with creators, and interact with each other. The work spans some of the most bold bets in applied GenAI, including:

• Building the post-training, evaluation, and serving systems that turn frontier LLMs into reliable, high-quality product experiences used by billions.
• Building a general-purpose agentic platform that powers a wide range of GenAI products across Facebook — enabling teams to ship faster, safer, and at scale.
• Building systems that enable 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, advance their capabilities in understanding, generation, and grounding, and enable 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 forward the state of the art in multimodal reasoning and generation research, and prioritize research that can be directly applied to Meta’s product development.

Requirements & Tech Stack

• Work spans 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.
• Experience working in AI/ML environments.
• Can manage data pipelines and versioning.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, 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).