Sr Lead Software Engineer, Full Stack (AI) - Shopping (Remote-Eligible) Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One Shopping, you'll be part of a big group of makers, breakers, doers and disruptors, who love to solve real problems and meet real customer needs. We are seeking a Senior Lead Software Engineer who is passionate about marrying data with emerging technologies. As a full stack engineer focusing on AI, you'll have the opportunity to be on the forefront of driving a major transformation within Capital One Shopping. What You'll Do: - Be a hands-on leader driving the development of next-generation AI solutions for the future of shopping. - Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, mentoring other members of the engineering community. - Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment - Utilize programming languages like JavaScript, HTML/CSS, TypeScript, SQL, Python, and Go, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services Basic Qualifications: - Bachelor's Degree - At least 6 years of experience in software engineering (Internship experience does not apply) - At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud) Preferred Qualifications: - Expert-level proficiency in MCP, AI Tooling, and/or LLMs - Startup experience with a history of shipping products at high speed. - 9+ years of experience in at least one of the following: JavaScript, Java, TypeScript, SQL, Python, or Go - 4+ years of experience with AWS, GCP, Microsoft Azure, or another cloud service - 4+ years of experience in open source frameworks 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. Remote (Regardless of Location): $209,000 - $238,500 for Sr. Lead Software Engineer McLean, VA: $229,900 - $262,400 for Sr. Lead Software 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. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. 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).

• Own end-to-end delivery of production AI and ML systems from experimentation to deployment • Train, fine-tune, and optimize machine learning models, including LLMs and open-weight models • Build and maintain training, data processing, and inference pipelines • Improve model performance across accuracy, latency, reliability, and cost • Implement MLOps best practices for deployment, monitoring, CI/CD, and automated retraining • Develop evaluation frameworks, benchmark datasets, and quality checks for production models • Design and maintain scalable APIs and services that expose AI capabilities • Collaborate with Product, Backend, and Frontend teams to integrate AI into customer-facing workflows • Monitor production systems and continuously improve model and infrastructure performance • Research and evaluate emerging AI techniques, tools, and frameworks

• Run stakeholder interviews and map priorities during an initial discovery phase. • Produce a clear findings report that the client's leadership can act on. • Help the client decide on the highest priority area to build first. • Take the agreed priority from design through to a working build. • Structure delivery in phases, with clear deliverables at agreed milestones. • Ensure technical documentation is created and kept up to date throughout. • Lead a small technical team (2 to 4 people) through a time-boxed delivery. • Translate business priorities into a technical plan, and technical constraints back into business terms, for the client's leadership. • Act as the client's primary technical point of contact for the engagement.

• Define technical strategy across multiple AI initiatives, driving architectural decisions, influencing product and research direction, and aligning engineering investments across teams to maximize long-term business and clinical impact. • Design, build, train, evaluate and improve advanced machine learning and LLM-based systems for patient and provider-facing products (e.g., conversational AI, personalization, user understanding, clinical decision support, chronic care management). • Own problems end-to-end: scope the problem with clinicians and product partners, build datasets and evaluations, iterate on modeling, and ship to production with the right monitoring and guardrails. • Develop robust evaluation frameworks — offline benchmarks, human-in-the-loop review, online experiments — that give us confidence our models are safe, accurate, and improving over time. • Build and improve the platform that lets the team move quickly: data pipelines, training and inference infrastructure, prompt and model management, and tooling for clinical reviewers. • Partner closely with clinicians, product, and engineering to translate medical and operational requirements into ML problems and ship measurable improvements to patient and clinician experience. • Set technical direction for your area, mentor other engineers, and raise the bar on engineering and scientific rigor. The scope of leadership scales with seniority. • Stay close to the literature and the rapidly evolving AI ecosystem; bring back what is most useful for our patients and our team.