Applied Researcher 4 (AI Foundations)
$219k - $250k • San Jose, CA • Full-time
Posted 17d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Research Engineer
About the job
Capital One is seeking an Applied Researcher to join the AI Foundations team, focusing on building trustworthy and reliable AI systems that are transforming the banking industry. This role involves partnering with cross-functional teams to deliver AI-powered products, leveraging a broad stack of technologies to analyze vast amounts of data, and building AI foundation models through all phases of development. You will engage in high-impact applied research, translating complex work into tangible business goals, and collaborating with senior researchers on novel methods and findings. The ideal candidate is innovative, creative, and technically adept, with a deep understanding of AI methodologies and experience building large deep learning models.
Responsibilities
- Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
- Utilize technologies like PyTorch, AWS Ultraclusters, Huggingface, and Lightning to extract insights from data.
- Build AI foundation models from design through training, evaluation, validation, and implementation.
- Conduct applied research to advance AI capabilities for customer experiences.
- Translate complex technical work into clear business goals.
- Collaborate with senior researchers to prepare technical reports and conference submissions.
Requirements
- Currently have, or be in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, or an M.S. with 2 years of Applied Research experience.
- Deep understanding of AI methodologies.
- 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.
- Experience delivering libraries, platform code, or solution code to existing products.
- Track record of high-quality ideas or improvements in machine learning, demonstrated by publications or projects.
- Ability to own and pursue a research agenda autonomously.
- Experience with LLMs.
- Experience with NLP or industrial NLP research.
- Experience with pre-training of large language models.
- Experience training a large language model from scratch.
- Publications in deep learning theory or at top AI conferences (ACL, NAACL, EMNLP, Neurips, ICML, ICLR).
- Experience with optimization techniques for training and inference.
- Experience optimizing training for large models (10B+ parameters).
- Deep knowledge of deep learning algorithmic or optimizer design.
- Experience with compiler design.
- Experience with fine-tuning LLMs (Supervised Fine-Tuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning).
- Demonstrated knowledge of transfer learning, model adaptation, and model guidance principles.
- Experience deploying a fine-tuned large language model.