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We are seeking research engineers to join the Product and Applied Research (PAR) Real Time AI group within Meta Superintelligence Labs (MSL). As a member of the PAR RealTime AI group, you will develop Large Language Models and foundational technology to build Meta’s AI Characters products. This is a high-impact Individual Contributor (IC) role focused on LLM model post-training, developing RAG/tool calling solutions, and partnering closely with cross functional teams to build the real-world experiences. We work directly with the product team and bring our state-of-the-art technology across FoA, and the entire Meta creator and developer ecosystem. If you’re excited about building the future of immersive real-time AI characters and passionate about making a tangible impact on billions of users, we invite you to join us.
Job Responsibility:
Collaborate with cross-functional teams to develop Meta’s AI Characters products
Lead the development of new algorithms and systems for LLM post-training, evaluation and efficiency
Support creative data sourcing, high-quality post-training data curation, and scale and optimize data pipelines for large language models (LLMs)
Develop and integrate models,orchestrations and RAGs in production
Analyze and interpret experimental results, iterate on model architectures, and drive continuous improvement
Lead complex technical projects end-to-end
Requirements:
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
2+ years of industry experience in LLM/NLP, audio, or related AI/ML models
Experience as a formal technical lead, leading major technical initiatives with cross functional partners to impact, and/or influencing strategy across multiple teams
Skilled in model training, data, or inference & efficiency for LLMs
Experience building products/systems based on machine learning, reinforcement learning and/or deep learning methods
Programming experience in Python and hands-on experience with frameworks like PyTorch
Nice to have:
Experience with Reinforcement Learning from Human Feedback, reward modeling, or other LLM post-training techniques
Experience manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, or similar