AI Research Engineer, VLLM (vision large language models) - Generative AI

Meta•Menlo Park, CA

6h•$74 - $217,000

About The Position

Meta is seeking a Research Engineer to join our Llama Large Language Model (LLM) Research team. We are looking for recognized experts in VLLMs; with experience in areas like vision encoders, data filtering/curation for pre and post-training, RLHF, responsible AI and model controllability. The ideal candidate will have an interest in producing and applying new science/systems/technologies to help us develop and responsibly release vision large language models.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
Masters in AI, computer science, or related technical fields.
3+ years of Experience holding an industry, faculty, or government researcher position.
Publications in machine learning, computer vision, NLP, Audio
Experience writing software and executing complex experiments involving large AI models and datasets.
Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.

Nice To Haves

Industry research & development experiences in generative AI and LLM research.
Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward.
Fluent in Python and PyTorch (or equivalent).
Publication track record at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL).
PhD in AI, computer science, or related technical fields.

Responsibilities

Lead, collaborate, and execute on developing scalable and effective data curation, model development and eval systems that push forward the state of the art in multimodal reasoning and generation research.
Work towards long-term ambitious research/development goals, while identifying intermediate milestones.
Directly contribute to experiments, including designing experimental details, writing reusable code, running evaluations, and organizing results.
Work with a large team.
Mentor other team members. Play a significant role in healthy cross-functional collaboration.
Prioritize research and development that can be applied to Meta's product development.