Kodiva

Research Engineer

Posted Yesterday

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Palo Alto, CA, United States

Hybrid

Mid level

Generative AI

The Role

Conduct applied machine learning research focused on language and multimodal models. Develop and evaluate novel methods for language understanding, generation, reasoning, adaptation, and continual learning. Build and optimize LLM and VLM systems using Python and deep learning frameworks, including distributed training. Communicate quantitative research findings to technical audiences and contribute through publications, patents, open-source work, or deployed systems.

Summary Generated by Built In

Minimum Qualifications

3+ years of combined research and engineering experience in machine learning, including hands-on work with language models.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

5+ years of experience conducting applied ML research, with a sustained record of taking novel methods from experimentation to working systems.
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

Skills Required

3+ years of combined research and engineering experience in machine learning, including hands-on language model work
Direct experience with Large Language Models or Vision-Language Models in research or production settings
Strong research background in Natural Language Processing, Machine Learning, or a related discipline focused on language modeling
Experience solving complex language understanding, generation, or multimodal AI problems using rigorous quantitative methodologies
Ability to clearly communicate research findings to diverse technical audiences
Proficiency in Python and deep learning frameworks such as PyTorch, JAX, or TensorFlow
Experience with distributed training and model optimization
5+ years of experience conducting applied machine learning research
PhD in Computer Science, Computational Linguistics, or a closely related field focused on language models and adaptive learning systems
Research and engineering experience with LLMs or VLMs, particularly meta-learning or parameter-efficient adaptation
First-authored publications on language models, meta-learning, hypernetworks, or adaptive AI in recognized conferences or journals
Kaggle experience
Research expertise in LLM reasoning, hypernetworks, multi-task learning, novel LLM adaptation methods, or online continual learning