Applied Researcher I (AI Foundations)

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 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. 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, Hugging Face, 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, 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.
• Translate the complexity of your work into tangible business goals using strong interpersonal skills.

Requirements

• 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 the scheduled start date), or an M.S. in the same fields plus 2 years of experience in Applied Research.

Qualifications

The ideal candidate:

• Loves the process of analyzing and creating while sharing a passion to do the right thing for customers.
• Is innovative, continually researching and evaluating emerging technologies, staying current on published state-of-the-art methods, and seeking opportunities to apply them.
• Is creative, thriving on bringing definition to big, undefined problems, asking questions, and pushing hard to find answers.
• Is a leader, challenging conventional thinking and working with stakeholders to improve the status quo, with a passion for talent development.
• Is technical, comfortable with open-source languages, and passionate about developing further with hands-on experience in 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, demonstrated by a track record of delivering models at scale in terms of 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 high-quality ideas or improvements in machine learning, demonstrated by first-author publications or projects.
• Can own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
• LLM-focused PhD with a focus on NLP or a 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 conferences such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• Optimization (Training & Inference):
  • PhD focused on 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+ 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).
  • Demonstrated knowledge of principles of transfer learning, model adaptation, and model guidance.
  • Experience deploying a fine-tuned large language model.

Pay

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

• Cambridge, MA: $218,700 - $249,600 (Applied Researcher I)
• McLean, VA: $218,700 - $249,600 (Applied Researcher I)
• New York, NY: $238,600 - $272,300 (Applied Researcher I)
• San Jose, CA: $238,600 - $272,300 (Applied Researcher I)

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 (LTI).

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.