Applied Researcher II

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, we have 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 bring 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. You will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses.

About the team

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 collaborate with product, technology, and business leaders to apply the state of the art in AI to our business.

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 insights hidden within huge volumes of numeric and textual data.
• Build AI foundation models through all phases of development: design, 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.

Qualifications

The ideal candidate:

• Loves the process of analyzing and creating, but also shares our passion to do the right thing for our customers.
• Is innovative—continually researches and evaluates emerging technologies, stays current on published state-of-the-art methods, and seeks opportunities to apply them.
• Is creative—thrives on bringing definition to big, undefined problems, asks questions, and pushes hard to find answers.
• Is a leader—challenges conventional thinking, works with stakeholders to improve the status quo, and is passionate about talent development.
• Is technical—comfortable with open-source languages and passionate about developing further. Has hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
• Has a deep understanding of AI methodologies, experience building large deep learning models (language, images, events, or graphs), and expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
• Has an engineering mindset with a track record of delivering models at scale in both training data and inference volumes.
• Has experience delivering libraries, platform-level code, or solution-level code to existing products.
• Has a professional track record of new ideas or improvements in machine learning, demonstrated by first-author publications or projects.
• Possesses the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.

Requirements

• Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields (required degree must be obtained on or before the scheduled start date) plus 2 years of experience in Applied Research.
• OR an M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research.

Preferred Qualifications

• LLM:
  • PhD focus on NLP or Master’s 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 a team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens).
  • Publications in deep learning theory or at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• Behavioral Models:
  • 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, or ICLR.
  • Experience scaling graph models to greater than 50 million nodes.
  • Experience with large-scale deep learning-based recommender systems or production real-time and streaming environments.
  • Contributions to common open-source frameworks (e.g., PyTorch-Geometric, DGL).
  • Proposed new methods for inference or representation learning on graphs or sequences.
  • Worked with datasets of 100 million+ users.
• Optimization (Training & Inference):
  • PhD focused on topics related to optimizing training of very large deep learning models.
  • Multiple years of experience and/or publications on model sparsification, quantization, training parallelism/partitioning design, gradient checkpointing, or model compression.
  • Experience optimizing training for a 10B+ parameter model.
  • Deep knowledge of deep learning algorithmic and/or optimizer design.
  • Experience with compiler design.
• Finetuning:
  • 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.
• Data Preparation:
  • 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.

Pay

The annual full-time salary ranges for this role by location are:

• McLean, VA: $262,500 - $299,600
• New York, NY: $286,400 - $326,800
• Cambridge, MA: $262,500 - $299,600
• San Francisco, CA: $286,400 - $326,800
• San Jose, CA: $286,400 - $326,800

Candidates hired to work in other locations will be subject to the pay range associated with that location. This role is also eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

Benefits

Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. Learn more at the Capital One Careers website.