Applied Researcher I

Capital One • Naval Academy, MD

226d • $214,500 - $267,000

About The Position

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.

Requirements

• Currently has, or is in the process of obtaining, a PhD, with an expectation that required degree will be obtained on or before the scheduled start date or M.S. with at least 2 years of experience in Applied Research.

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields.
• LLM 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 (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization).
• Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens).
• Publications in deep learning theory.
• Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR.
• PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series).
• Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR.
• Worked on scaling graph models to greater than 50m nodes.
• Experience with large scale deep learning based recommender systems.
• Experience with production real-time and streaming environments.
• Contributions to common open source frameworks (pytorch-geometric, DGL).
• Proposed new methods for inference or representation learning on graphs or sequences.
• Worked datasets with 100m+ users.
• PhD focused on topics related to optimizing training of very large deep learning models.
• Multiple years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression.
• Experience optimizing training for a 10B+ model.
• Deep knowledge of deep learning algorithmic and/or optimizer design.
• Experience with compiler design.
• PhD focused on topics related to guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning).
• Demonstrated knowledge of principles of transfer learning, model adaptation and model guidance.
• Experience deploying a fine-tuned large language model.
• Publications studying tokenization, data quality, dataset curation, or labeling.
• Contribution to a major open source corpus.
• Contribution to open source libraries for data quality, dataset curation, or labeling.

Responsibilities

• Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money.
• Leverage a broad stack of technologies - Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more - to reveal the insights hidden within huge volumes of numeric and textual data.
• Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.
• Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences.
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

Benefits

• Comprehensive health benefits.
• Financial benefits.
• Inclusive set of benefits that support total well-being.

What This Job Offers

• Career Level: Entry Level
• Industry: Credit Intermediation and Related Activities
• Education Level: Ph.D. or professional degree