Applied Researcher 5 (AI Foundations, LLM Core and Agentic AI)
$263k - $300k • 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 Research 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 creating 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 build AI foundation models through all phases of development. The position requires engaging in high-impact applied research to push the boundaries of AI for next-generation customer experiences and translating complex work into tangible business goals. You will also lead research threads that bridge model development and deployment, ensuring scientific insights translate to production impact.
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 data.
- Build AI foundation models from design through training, evaluation, validation, and implementation.
- Conduct applied research to integrate the latest AI developments into customer experiences.
- Translate complex technical work into tangible business goals.
- Lead research threads connecting prototype model development with ML engineering for production impact.
Requirements
- PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, or MS with 2 years of experience in Applied Research.
- 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 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.
- Multiple publications on pre-training large language models.
- Experience as a member of a team that has trained 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 for training and inference.
- Experience with 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 Fine-Tuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning).
- Demonstrated knowledge of transfer learning principles.