Applied Researcher 4 (AI Foundations, LLM Customization and Finetuning)
$219k - $250k • San Jose, CA • Full-time
Posted 26d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
LLM Engineer
About the job
Capital One is seeking an Applied Researcher to join the AI Foundations team, focusing on LLM customization and finetuning. This role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and Huggingface to analyze large datasets. You will be instrumental in building AI foundation models through all development phases, from design to implementation, and engaging in applied research to advance customer experiences. The position requires translating complex work into tangible business goals and collaborating with senior researchers on technical reports and conference submissions.
Responsibilities
- Partner with data scientists, software engineers, ML engineers, and product managers to deliver AI-powered products.
- Leverage technologies like Pytorch, AWS Ultraclusters, Huggingface, and Lightning to analyze large volumes of data.
- Build AI foundation models through design, training, evaluation, validation, and implementation.
- Conduct high-impact applied research to advance customer experiences with the latest AI developments.
- Translate the complexity of work into tangible business goals.
- Collaborate with senior researchers to prepare internal tech reports or conference submissions.
Requirements
- PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, or an M.S. with 2 years of Applied Research experience.
- Experience building large deep learning models (language, images, events, or graphs).
- Expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF.
- Engineering mindset with a track record of delivering models at scale for training data and inference volumes.
- Experience delivering libraries, platform-level code, or solution-level code to existing products.
- Track record of generating high-quality ideas or improving existing ML ideas, demonstrated by publications or projects.
- Ability to own and pursue a research agenda, including identifying impactful problems and autonomously carrying out long-running projects.
- Familiarity with open-source languages and developing AI foundation models and solutions using open-source tools and cloud platforms.
- Deep understanding of AI methodologies.
- PhD focus on NLP or Masters with 5 years of industrial NLP research experience (preferred).
- Multiple publications on pre-training large language models (preferred).
- Experience training a large language model from scratch (10B+ parameters, 500B+ tokens) (preferred).
- Publications in deep learning theory (preferred).
- Publications at ACL, NAACL, EMNLP, Neurips, ICML, or ICLR (preferred).
- Experience with LLM finetuning, including supervised finetuning, instruction-tuning, dialogue-finetuning, and parameter tuning (preferred).
- Demonstrated knowledge of transfer learning, model adaptation, and model guidance principles (preferred).
- Experience deploying a fine-tuned large language model (preferred).
- Publications studying tokenization, data quality, dataset curation, or labeling (preferred).
- Contribution to a major open-source corpus or libraries for data quality, dataset curation, or labeling (preferred).
- Ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks.
- Experience designing controlled experiments and documenting reproducibility results.