Distinguished Engineer, Generative AI Systems (Remote Eligible) at Capital One
McLean, VA 22107
About the Job
Distinguished Engineer, Generative AI Systems (Remote Eligible)
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 Senior Distinguished Engineer, AI Systems, to help us build the foundations of our enterprise AI Capabilities. You will work on a wide range of initiatives, whether that’s designing robust, secure infrastructure, building large-scale distributed training clusters, deploying LLMs on GPU instances for real-time use cases, or supporting cutting-edge AI research and development, all in our public cloud infrastructure. You will work with a team of AI engineers and researchers to envision the target state of our capabilities while helping to design and implement key services. Examples of projects you will work on include:
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 infrastructure for serving large ML models, in our public cloud.
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 implement benchmarks to measure the performance of software systems within AI capabilities and make recommendations on technology selection
Develop applications that leverage LLMs and FMs.
Design and implement capabilities to support MLOps for foundation models.
Capital One is open to hiring a Remote Employee for this opportunity
Basic Qualifications:
Bachelor's degree in Computer Science, Computer Engineering or a technical field
At least 7 years of experience designing and building distributed computing HPC and large-scale ML systems
At least 5 years of experience developing AI and ML algorithms in Python or C/C++
At least 3 years of experience with the full ML development lifecycle using AI and ML frameworks and public cloud.
Preferred Qualifications:
Master’s degree or PhD in Engineering, Computer Science, a related technical field, or equivalent practical experience with a focus on modern AI techniques.
Experience designing large-scale distributed platforms and/or systems in cloud environments such as AWS, Azure, or GCP.
Experience architecting cloud systems for security, availability, performance, scalability, and cost.
Experience with delivering very large models through the MLOps life cycle from exploration to serving.
Experience with building GPU clusters in the public cloud with tightly-coupled storage and networking.
Experience with the complete stack for distributed training of large models including ML compilers, distributed training frameworks, and ML development frameworks such as Pytorch, Tensorflow, Lightning etc.
Experience with one or multiple areas of AI technology stack including prompt engineering, guardrails, vector databases/knowledge bases, LLM hosting and fine-tuning.
Authored research publications in top peer-reviewed conferences, or industry-recognized contributions in the space of neural networks, distributed training and SysML.
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
The minimum and maximum full-time annual salaries for this role are listed below, by location. 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. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
New York City (Hybrid On-Site): $274,800 - $313,600 for Distinguished Machine Learning Engineer
San Francisco, California (Hybrid On-Site): $291,100 - $332,300 for Distinguished Machine Learning Engineer
Remote (Regardless of Location): $232,900 - $265,800 for Distinguished Machine Learning Engineer
Candidates 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.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
--s-p-m1--
By applying, you consent to your information being transmitted by Monster to the Employer, as data controller, through the Employer’s data processor SonicJobs.
See Capital One Terms & Conditions at https://www.capitalone.com/digital/terms-conditions/ and Privacy Policy at https://www.capitalone.com/privacy/ and SonicJobs Privacy Policy at https://www.sonicjobs.com/us/privacy-policy and Terms of Use at https://www.sonicjobs.com/us/terms-conditions