Research Scientist - Data
$150k - $450k • Sunnyvale, CA • Full-time
Posted 1y ago
Job Location
Sunnyvale, CA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Research Engineer
About the job
We are a dedicated research lab focused on building, understanding, using, and risk-managing foundation models. Our mission is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy. As part of our team, you will work on cutting-edge foundation model training alongside world-class researchers, data scientists, and engineers, tackling fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries, establishing our institution as a global hub for high-performance computing in deep learning.
Responsibilities
- Pioneer web-scale data collection and curation methodologies for LLMs and multi-modal foundation models.
- Design and implement novel data synthesis pipelines for code, mathematics, and agentic reasoning datasets.
- Trace the impact of data from pre-training to final model capabilities and create automated quality assessment frameworks for massive datasets.
- Design data recipes that maximize model capabilities across diverse domains.
- Optimize data-model co-design for improved training dynamics.
- Contribute to research papers and represent the institution at industry conferences and events.
Requirements
- Master's in Computer Science, Data Science, or a related technical field, or equivalent practical experience.
- PhD or equivalent research experience in Machine Learning, NLP, or Data Science with a focus on LLMs and data is preferred.
- Experience working with large language models, including evaluation, fine-tuning, and prompt engineering.
- Strong Python development skills with a focus on research-grade code and scalable data pipelines.
- Familiarity with collecting and processing large-scale datasets from open-source and web resources.
- Demonstrated ability to work with ML infrastructure (e.g., model evaluation, optimization, debugging).
- Proactive mindset with the ability to identify impactful research questions and execute on them with minimal supervision.
- Effective communication and collaboration skills for working in cross-functional teams.
- Prior research experience in areas such as web data curation and mixing, synthesizing complex datasets for training, LLM evaluation, post-training data, efficient inference, LLM-as-a-judge, tokenization is preferred.
- Strong publication record in leading AI conferences or prior contributions to open-source AI research or data tools is preferred.
- Hands-on experience training language/multi-modal models from scratch is preferred.