Research Engineer

Location Palo Alto, CA Employment Type Full time Location Type On-site Department Engineering Role Overview We are seeking an AI Researcher specializing in Language Models to join our dynamic team advancing the frontiers of large language models and vision-language models. The ideal candidate will have hands‑on experience with LLMs or VLMs, particularly in adaptive learning approaches using meta‑learning and hypernetworks. This role involves developing novel architectures for dynamic model adaptation, improving model reasoning and generalization, and effectively communicating breakthroughs to both research and engineering teams. Minimum Qualifications Direct experience working with Large Language Models (LLMs) or Vision‑Language Models (VLMs) in research or production settings. Strong research background in Natural Language Processing, Machine Learning, or related disciplines with focus on language modeling. Proven track record in solving complex problems in language understanding, generation, or multimodal AI using rigorous quantitative methodologies. Demonstrated ability to clearly communicate research findings to diverse technical audiences. Proficient programming skills in Python and deep learning frameworks (PyTorch, JAX, or TensorFlow), with experience in distributed training and model optimization. Preferred Qualifications PhD in Computer Science, Computational Linguistics, or closely related field with focus on language models and adaptive learning systems. Proven research and engineering experience with LLMs/VLMs, particularly in meta‑learning or parameter‑efficient adaptation, as evidenced by grants, fellowships, patents, internships, or contributions to open‑source projects. First‑authored publications on language models, meta‑learning, hypernetworks, or adaptive AI in recognized peer‑reviewed conferences (ACL, EMNLP, NeurIPS, ICML, ICLR) or journals. Preferred Technical Experience Research expertise in LLM reasoning, hypernetworks, multi‑task learning, meta‑learning, designing novel LLM adaptation methods, Online Continual Learning. #J-18808-Ljbffr