AI Research Scientist, Personalization, SuperIntelligence Labs
3 hours ago
Meta is hiring AI Research Scientists to join our team dedicated to enhancing the personalization of Meta AI. We are seeking experts in Large Language Model (LLM) post-training techniques to improve the relevance and individuality of LLM responses.
Our work encompasses:
* Developing and refining post-training recipes.
* Designing evaluations for personalized responses.
* Implementing effective tool calling mechanisms.
This role involves tackling frontier challenges at Meta's scale, including synthetic data curation, developing advanced reinforcement learning (RL) environments and recipes, and sophisticated user profiling to redefine personalization.
Responsibilities
- Collaborate with cross-functional teams to develop and improve personalization in Meta's frontier foundation models
- Directly contribute to experiments, including designing experimental details, authoring reusable code, running evaluations, and organizing results
- Prioritize research that can be applied to Meta's product development
- Lead complex research projects end-to-end
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Phd in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Experience in Generative AI models and building LLM technologies particularly post training
- Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward. Proven experience of proactively identifying, scoping and implementing innovative research solutions
- Programming experience in Python and hands-on experience with frameworks like Pytorch, Spark
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment
- Hands on experience in Personalization with LLMs particularly on designing agentic systems, RAG and Reinforcement learning based post training techniques
- First author publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL)
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