Applied Researcher 4 (AI Foundations, Graph and Behavioral Models)
$219k - $250k • San Jose, CA • Full-time
Posted 1mo 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 creating 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 uncover insights from vast datasets, and building AI foundation models through all development phases. You will engage in high-impact applied research to push the boundaries of AI for customer experiences and translate complex technical work into tangible business goals. The ideal candidate is innovative, creative, a leader, and technically proficient 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.
- Leverage technologies like PyTorch, AWS Ultraclusters, and Huggingface to analyze large volumes of numeric and textual 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 tangible business goals.
- Collaborate with senior researchers to prepare technical 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.
- Experience delivering libraries, platform-level code, or solution-level 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.
- Comfort with open-source languages and developing AI foundation models 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 or at top AI conferences (preferred).
- PhD focus on geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series) (preferred).
- Multiple papers on training models on graph and sequential data structures (preferred).
- Experience scaling graph models to >50m nodes (preferred).
- Experience with large-scale deep learning-based recommender systems (preferred).
- Experience with production real-time and streaming environments (preferred).
- Contributions to common open-source graph frameworks (e.g., pytorch-geometric, DGL) (preferred).
- Proposed new methods for inference or representation learning on graphs or sequences (preferred).
- Worked with datasets of 100m+ users (preferred).
- Experience with Finetuning LLMs (preferred).