Job description

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. Why Join Us - Product LLM work at singular scale - Your post-training decisions, evaluation frameworks, and serving architecture directly affect billions of daily interactions. - End-to-end ownership - We don't hand off models to a separate product team. We own the loop from training data to production behavior to measurement. The impact of your work shows up in days, not quarters. - The problems are unsolved - How do you evaluate open-ended conversational AI at scale? How do you fine-tune for groundedness across millions of varied creator profiles? These aren't incremental improvements, they're open research questions with immediate product consequences. - Our team is hands-on, with high autonomy, working on critical bets - We're deliberately keeping this team lean and experienced. You'll have outsized influence on technical direction, not just execution. Depending on your interests and strengths, your 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 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 Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Experience with large scale model training, implementing algorithms, and evaluating speech-based systems 5+ YOE as an Applied AI Research Scientist or Applied AI Research Engineer PhD in Computer Science, Computer Engineering, or relevant technical field Experience taking ideas from research to production

Requirements

Must-haves

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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillm

Platform: meta
Effort: medium