Research Engineer, Speech & Language - Generative AI

We are looking for a Research Engineer (Speech and Language) to join the GenAI Speech team at Meta. The GenAI Speech team works on speech-LLMs, and leverages knowledge in areas like speech encoders, pre-training, post-training, reinforcement learning, LLM alignment, multi-modal, modeling, speech and audio processing, speech recognition, speech synthesis, and multilingual modeling.

Qualifications: Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Experience in one or more of the following areas: machine learning, large language models, speech and audio processing, speech generation, or related fields Experience developing machine learning algorithms or machine learning infrastructure in Python, PyTorch, and/or C/C++ 4+ year(s) of work experience in an industry, university, or government lab Direct experience in generative AI and LLM research The ideal candidate will have industry experience working on Speech, Language, and LLM related topics and the experience to apply relevant AI and machine learning techniques to build intelligent rich speech & language systems that improve Meta's products and experiences. A MSc or PhD in Artificial Intelligence (AI), computer science, or related technical fields. Experience with large scale model training, implementing algorithms, and evaluating systems Candidates should have an interest in taking new research findings in this area and implementing them towards product needs. Publication track record at peer-reviewed AI conferences (e.g. e.g., NeurIPS, ICML, ICLR, ICASSP, Interspeech)

Responsibilities: Apply relevant AI and machine learning techniques to build speech-LLM technology that will improve Meta products and experiences Develop novel, accurate AI algorithms and advanced systems for large scale applications Directly contribute to experiments, including designing experimental details, developing reusable code, running evaluations, and organizing results Work with large data, and contribute to development of large scale foundation models Design methods, tools, and infrastructure to push forward the state of the art in large language models