AI Research Engineer
Confirmed on the employer's own hiring board on Oct 16, 2026. First seen by Alion on Mar 27, 2026. Meta scores B on the Alion truth index.
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
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
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) Experience working in AI/ML environments Can manage data pipelines and versioning