Experience with Large Language Models (LLMs) or Vision-Language Models (VLMs) in either research or production settings
Strong background in Natural Language Processing or Machine Learning focused on language modeling
Proven problem-solving skills in language understanding and multimodal AI using quantitative methods
Ability to effectively communicate research findings to diverse technical audiences
Proficient in Python and deep learning frameworks (e.g., PyTorch, JAX, TensorFlow) with experience in distributed training.
Responsibilities
Influence the growth of a high conviction AI company at an early stage
Build a capable AI team from the ground up
Engage in research and development of advanced AI models
Communicate findings to both technical and non-technical stakeholders
Implement solutions based on rigorous quantitative analysis.
Benefits
Opportunity to shape the company's trajectory
Work in a high-impact, early-stage AI environment
Collaborate with talented professionals in the field
Contribute to meaningful projects that span multiple industries.
Full Job Description
Role Description
This role is on-site in Palo Alto, CA.
The work we are doing has meaningful impact across industries, and every hire at Nace AI plays a critical role in shaping the company's trajectory. This is a unique opportunity to join a high conviction AI company at an early stage and directly influence its growth.
If building a world-class AI team from the ground up excites you, we'd love to talk.
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
Kaggle experience is a plus.
Preferred Technical Experience
Research expertise in LLM reasoning, hypernetworks, multi-task learning, meta-learning, designing novel LLM adaptation methods, Online Continual Learning
