![]() ![]() This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Lead Machine Learning EngineerĬandidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. The minimum and maximum full-time annual salaries for this role are listed below, by location. Experience at tech and product-driven companies/startups preferred.Ībility to iterate rapidly with researchers and engineers to improve a product experience while building the foundational capabilities.įamiliarity with deploying large neural network models in demanding production environments.Įxperience with building GPU clusters in the public cloud with tightly-coupled storage and networking.Īt this time, Capital One will not sponsor a new applicant for employment authorization for this position. Master's or Doctoral degree in Computer science, Computer Engineering, Electrical engineering, Mathematics, or a similar field.īackground in machine learning with experience in large scale training and deployment of deep neural nets and/or transformer architectures.Įxperience with machine learning frameworks such as TensorFlow or Pytorch, Lightning, Mosaic ML etc.Ībility to move fast in an environment with ambiguity at times, and with competing priorities and deadlines. Examples of projects you will work on:ĭeploy 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.ĭesign 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.ĭesign and build run-time infrastructure for serving large ML models such as LLMs and FMs in our public cloud.īuild infrastructure for deploying search indexes and embeddings in vector databases that will work closely with the rest of our capabilities.Ĭapital One is open to hiring a Remote Employee for this opportunity.īachelor's degree in Computer Science, Computer Engineering or a technical fieldĪt least 8 years of experience designing and building data-intensive solutions using distributed computingĪt least 4 years of experience with HPCs, vector embedding, or semantic search technologiesĪt least 4 years of experience programming with Python, Scala, or JavaĪt least 3 years of experience building, scaling, and optimizing training and inferencing systems for deep neural networks 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. ![]() 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. ![]() Lead Engineer, Generative AI Infrastructure to help us build the foundations of our AI capabilities. ![]() 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 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. Because of our investments in public cloud infrastructure and machine learning platforms, we are now uniquely positioned to harness the power of AI. 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. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. Our mission at Capital One is to create trustworthy, reliable and human-in-the-loop AI systems, changing banking for good. Senior Lead Engineer - Generative AI Infrastructure (Remote-Eligible) Center 3 (19075), United States of America, McLean, Virginia ![]()
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