Applied Researcher 4 (AI Foundations, LLM Core and Agentic AI)
$219k - $250k • New York, NY • Full-time
Posted 1mo ago
Job Location
New York, NY
Also open in San Jose, CA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Engineer
About the job
Capital One is seeking an Applied Researcher to join the AI Foundations team, focusing on LLM Core and Agentic AI. This role involves building trustworthy and reliable AI systems that enhance customer experiences in banking. You will partner with cross-functional teams to deliver AI-powered products, leverage a broad technology stack including PyTorch and Huggingface, and engage in high-impact applied research to push the boundaries of AI. The ideal candidate is innovative, creative, and possesses a deep understanding of AI methodologies, with a track record of delivering large deep learning models and contributing to the advancement of AI through research and publications.
Responsibilities
- Partner with data scientists, software engineers, machine learning 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 all phases of development, from design to implementation.
- Engage in applied research to integrate the latest AI developments into customer experiences.
- Translate complex technical work into tangible business goals.
- Collaborate with senior researchers to prepare internal tech reports or 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.
- 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 generating high-quality ideas or improving existing machine learning 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.
- Comfort 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.
- Multiple publications on topics related to the pre-training of large language models.
- Experience training a large language model from scratch (10B+ parameters, 500B+ tokens).
- Publications in deep learning theory or at top AI conferences (ACL, NALCM, EMNLP, Neurips, ICML, ICLR).
- Experience with optimization (training & inference), model sparsification, quantization, training parallelism/partitioning design, gradient checkpointing, or model compression.
- Experience optimizing training for a 10B+ model.
- Deep knowledge of deep learning algorithmic and/or optimizer design.
- Experience with compiler design.
- Experience with fine-tuning LLMs (supervised, instruction, dialogue, parameter tuning).
- Demonstrated knowledge of transfer learning, model adaptation, and model guidance principles.
- Experience deploying a fine-tuned large language model.