Sr. Distinguished Applied Researcher

Center 1 (19052), United States of America, McLean, Virginia Sr.
Distinguished Applied Researcher Overview:
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.
Team Description:
The AI Foundations team is at the center of bringing our vision for AI at Capital One to life.
Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems.
We work with product, technology and business leaders to apply the state of the art in AI to our business.
This is an individual contributor (IC) role driving strategic direction through collaboration with Applied Science, Engineering and Product leaders across Capital One.
As a well-respected IC leader, you will guide and mentor a team of applied scientists and their managers without being a direct people leader.
You will be expected to be an external leader representing Capital One in the research community, collaborating with prominent faculty members in the relevant AI research community.
In this role, you will:
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.
The Ideal Candidate :
You love the process of analyzing and creating, but also share our passion to do the right thing.
You know at the end of the day it's about making the right decision for our customers.
Innovative.
You continually research and evaluate emerging technologies.
You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.
Creative.
You thrive on bringing definition to big, undefined problems.
You love asking questions and pushing hard to find answers.
You're not afraid to share a new idea.
A leader.
You challenge conventional thinking and work with stakeholders to identify and improve the status quo.
You're passionate about talent development for your own team and beyond.
Technical.
You're comfortable with open-source languages and are passionate about developing further.
You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
Has a deep understanding of the foundations of AI methodologies.
Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following:
training optimization, self-supervised learning, robustness, explainability, RLHF.
An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes.
Experience in delivering libraries, platform level code or solution level code to existing products.
A professional with a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.
Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.
Key Responsibilities:
Partner with a cross-functional team of scientists, machine learning engineers, software engineers, and product managers to deliver AI-powered platforms and solutions that change how customers interact with their money.
Basic Qualifications:
Ph.
D.
plus at least 6 years of experience in Applied Research or M.
S.
plus at least 8 years of experience in Applied Research Preferred Qualifications:
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields LLM PhD focus on NLP or Masters with 10 years of industrial NLP research experience Core contributor to team that has trained a large language model from scratch (10B parameters, 500B tokens) Numerous publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR 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) Has worked on an LLM (open source or commercial) that is currently available for use Demonstrated ability to guide the technical direction of a large-scale model training team Experience working with 500 node clusters of GPUs Has worked on LLM scaled to 70B parameters and 1T tokens Experience with common training optimization frameworks (deep speed, nemo) Behavioral Models PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series) Member of technical leadership for model deployment for a very large user behavior model 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 Optimization (Training & Inference) PhD focused on topics related to optimizing training of very large language models 5 years of experience and/or publications on one of the following topics:
Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression Finetuning PhD focused on topics related to guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, .