Applied Researcher 4

About the Role

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. 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.

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.

Key Responsibilities

• Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products.
• Leverage a broad stack of technologies — PyTorch, AWS Ultraclusters, Huggingface, Lightning, and more — to reveal insights from 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.
• Collaborate with senior researchers to prepare internal tech reports or conference submissions.

Requirements & Tech Stack

• PyTorch
• AWS Ultraclusters
• Huggingface
• Lightning
• LLM
• NLP
• Optimization (Training & Inference)
• Finetuning

Qualifications

• Currently have, or are in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research.
• You are comfortable with open-source languages and developing further.
• You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
• You possess 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 track record of coming up with high quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.
• Ability to 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.
• 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 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.