Senior Lead Engineer - Generative AI Infrastructure (Remote Eligible)

About Capital One

Over 25 years ago, Capital One revolutionized the credit card industry with data and technology. Today, we are one of the most widely recognized brands in banking — serving more than 100 million customers across a diverse set of businesses.

Position

Our mission at Capital One is to create trustworthy, reliable and human-in-the-loop 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. Because of our investments in public cloud infrastructure and machine learning platforms, we are now uniquely positioned to harness the power of AI. 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.

We are looking for an experienced Sr. Lead Engineer, Generative AI Infrastructure to help us build the foundations of our AI capabilities. You will work on a wide range of initiatives, whether that’s building large-scale distributed training clusters, or deploying LLMs on GPU instances for real-time applications and decisioning systems, or supporting cutting-edge AI research and development, all in our public cloud infrastructure. You will work closely with our cloud and container infrastructure teams as well as our world-class team of AI researchers to design and implement key capabilities.  Examples of projects you will work on: 

• Deploy a thousand-node training cluster optimizing storage and networking stack, with tightly coupled training pipelines to take advantage of multiple parallelism strategies, in our public cloud. 

• Design and build fault-tolerant infrastructure to support long-running large-scale training tasks reliably despite failure of individual nodes, using containers and check-pointing libraries. 

• Design and build run-time infrastructure for serving large ML models such as LLMs and FMs in our public cloud.

• Build infrastructure for deploying search indexes and embeddings in vector databases that will work closely with the rest of our capabilities. 

Capital One is open to hiring a Remote Employee for this opportunity.

Your qualification

Basic Qualifications:

• Bachelor's degree in Computer Science, Computer Engineering or a technical field

• At least 8 years of experience designing and building data-intensive solutions using distributed computing

• At least 8 years of experience programming with Python, Go, Scala, or Java

• At least 1 year of experience with HPCs, vector embedding, or semantic search technologies

• At least 1 year of experience building, scaling, and optimizing training or inferencing systems for deep neural networks 

Preferred Qualifications:

• Master's or Doctoral degree in Computer science, Computer Engineering, Electrical engineering, Mathematics, or a similar field.

• Background in machine learning with experience in large scale training and deployment of deep neural nets and/or transformer architectures.

• Experience with machine learning frameworks such as TensorFlow or Pytorch, Lightning, Mosaic ML etc.

• Ability to move fast in an environment with ambiguity at times, and with competing priorities and deadlines. 

• Experience at tech and product-driven companies/startups preferred. 

• Ability to iterate rapidly with researchers and engineers to improve a product experience while building the foundational capabilities.

• Familiarity with deploying large neural network models in demanding production environments. 

• Experience with building GPU clusters in the public cloud with tightly-coupled storage and networking. 

What we offer

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