Jobs for Humanity is partnering with Capital One to build an inclusive and just employment ecosystem. Therefore, we prioritize individuals coming from the following communities: Refugee, Neurodivergent, Single Parent, Blind or Low Vision, Deaf or Hard of Hearing, Black, Hispanic, Asian, Military Veterans, the Elderly, the LGBTQ, and Justice Impacted individuals. This position is open to candidates who reside in and have the legal right to work in the country where the job is located.

Company Name: Capital One

Senior Lead Engineer - Generative AI Infrastructure (Remote-Eligible) Our mission at Capital One is to create trustworthy, reliable, and inclusive AI systems that improve the banking experience for everyone. We have been at the forefront of using machine learning to provide real-time, intelligent, and automated customer experiences. Our applications of AI bring simplicity and humanity to banking, from detecting unusual charges to answering customer questions in real time. With 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 diverse and talented teams to drive breakthrough product experiences and scalable AI infrastructure. At Capital One, you will play a key role in bringing the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses. We are currently seeking an experienced Sr. Lead Engineer, Generative AI Infrastructure to help us build the foundations of our AI capabilities. In this role, you will work on various initiatives, such as building large-scale distributed training clusters, deploying real-time applications and decision systems, and supporting AI research and development. You will collaborate closely with our cloud and container infrastructure teams, as well as our world-class team of AI researchers, to design and implement key capabilities. Some examples of projects you will work on include: - Deploying a large training cluster in our public cloud, optimizing storage and networking for multiple parallelism strategies. - Designing and building fault-tolerant infrastructure using containers and check-pointing libraries to support long-running large-scale training tasks. - Developing run-time infrastructure for serving large ML models in our public cloud. - Building infrastructure for deploying search indexes and embeddings in vector databases. This is a remote-eligible position, allowing you to work from anywhere. We are committed to creating an inclusive and accessible work environment for everyone. Basic Qualifications: - Bachelor's degree in Computer Science, Computer Engineering, or a related technical field. - At least 8 years of experience designing and building data-intensive solutions using distributed computing. - At least 4 years of experience with HPCs, vector embedding, or semantic search technologies. - At least 4 years of programming experience with Python, Go, Scala, or Java. - At least 3 years of experience building, scaling, and optimizing training and inferencing systems for deep neural networks. Preferred Qualifications: - Master's or Doctoral degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related 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 work in a fast-paced environment with ambiguity and competing priorities. - Ability to collaborate with researchers and engineers to improve product experience while building 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. Capital One offers a comprehensive set of health, financial, and other benefits that support your total well-being. We are committed to diversity and inclusion in the workplace and are an equal opportunity employer. We consider applicants from all backgrounds and walks of life. If you are interested in this opportunity and meet the qualifications, please fill out the form below with your contact information and relevant experience. We look forward to hearing from you. Salary Range: - New York City (Hybrid On-Site): $230,100 - $262,700 for Sr. Lead Machine Learning Engineer - San Francisco, California (Hybrid On-Site): $243,800 - $278,200 for Sr. Lead Machine Learning Engineer - Remote (Regardless of Location): $195,000 - $222,600 for Sr. Lead Machine Learning Engineer Please note that this salary information is specific to each location and is subject to change. The actual salary will be communicated in your offer letter. To learn more about the benefits we offer, please visit the Capital One Careers website. Eligibility varies based on your employment status. This role is open for applications for a minimum of 5 business days. We appreciate your interest in joining our team. Thank you, [Your Name] Bullet Points: - Capital One is looking for a Senior Lead Engineer, Generative AI Infrastructure. - They are seeking someone with experience in building large-scale distributed training clusters, deploying real-time applications, and supporting AI research and development. - The role is remote-eligible, allowing you to work from anywhere. - Basic qualifications include a Bachelor's degree in Computer Science or a related field, at least 8 years of experience in building data-intensive solutions, and experience in deep neural networks. - Preferred qualifications include a Master's or Doctoral degree, background in machine learning, and familiarity with machine learning frameworks. - Capital One offers a comprehensive set of benefits and is committed to diversity and inclusion in the workplace. - The salary range varies depending on the location. Please fill out the form below with your contact information and relevant experience if you are interested in this opportunity.