Applied Researcher II (AI Foundations)

Overview At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. We are leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. Our applications of AI and ML bring humanity and simplicity to banking. We are building world-class applied science and engineering teams and scalable, high-performance AI infrastructure. You will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses. Team Description The AI Foundations team brings our AI vision to life, touching every aspect of the research lifecycle, from partnering with academia to building production systems. We work with product, technology, and business leaders to apply state-of-the-art AI to our business. 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 insights hidden within large 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 push the latest AI developments into the next generation of customer experiences. Translate the complexity of your work into tangible business goals. The Ideal Candidate You love the process of analyzing and creating, and strive to make decisions that are right for customers. Innovative. You continually research and evaluate emerging technologies and stay current on state-of-the-art methods, technologies, and applications. Creative. You thrive on defining big problems, asking questions, and sharing new ideas. A leader. You challenge conventional thinking and work with stakeholders to improve the status quo and develop talent. Technical. You are comfortable with open-source languages and have hands-on experience developing AI foundation models and solutions using open-source tools and cloud platforms. Has a deep understanding of the foundations of AI methodologies. Experience building large deep learning models, including language, images, events, or graphs, and expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF. An engineering mindset with a track record of delivering models at scale in terms of training data and inference volumes. Experience delivering libraries, platform-level, or solution-level code to existing products. A track record of new ideas or improvements in machine learning, evidenced by publications or notable projects. Ability to own and pursue a research agenda, including choosing impactful problems and carrying out long-running projects. Basic Qualifications Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields (degree must be obtained on or before start date plus 2 years of experience in Applied Research) OR an MS in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 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 5 years of industrial NLP research experience Multiple publications on topics related to pre-training of large language models 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 Behavioral Models PhD focus on geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series) Publications on training models on graph and sequential data 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 with datasets with 100M+ users Optimization (Training & Inference) PhD focused on topics related to optimizing training of very large deep learning models Experience on topics such as model sparsification, quantization, training parallelism/partitioning, 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 Finetuning PhD focused on guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning) Knowledge of transfer learning, model adaptation and guidance Experience deploying a fine-tuned large language model Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below by location. Salaries for part-time roles will be prorated based upon hours. Locations: Cambridge, MA; McLean, VA; New York, NY; San Francisco, CA; San Jose, CA. The salary ranges are provided for applicants hired to work in these locations and may vary by location. The actual offer will be in the candidate's offer letter. This role is also eligible to earn performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI). Incentives could be discretionary or non-discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable laws. Capital One promotes a drug-free workplace. If you require an accommodation, please contact Capital One Recruiting at View phone number on click.appcast.io or via email at View email address on click.appcast.io. All information provided will be kept confidential and used only to provide needed accommodations. View email address on click.appcast.io is for technical support or recruiting process questions. Capital One does not provide or guarantee third-party products or services information. Capital One Financial is composed of several entities; positions posted in different regions reflect corresponding entities. #J-18808-Ljbffr