Research Engineer

Department: Research

Location: Palo Alto, CA

Employment Type: FullTime

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