Jobs at Capital One

Conduct applied AI research and develop foundation models from design through production deployment. Collaborate with data scientists, software and machine learning engineers, and product managers to build scalable AI products. Research deep learning, language, graph, sequential, and behavioral models; optimize training and inference; reproduce emerging research; deliver platform or solution code; and publish internal or external technical findings.

Design, develop, deploy, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, lead technical governance and design reviews, define AI engineering standards, and mentor senior technical staff.

Conduct applied AI research and build foundation models from design through production deployment. Partner with data scientists, software engineers, machine learning engineers, and product managers to develop scalable AI products. Lead research initiatives, optimize training and inference, evaluate emerging methods, translate research into business outcomes, and deliver model libraries or platform code. Work may focus on large language models, deep learning optimization, fine-tuning, robustness, explainability, or reinforcement learning.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, model evaluation, and orchestration pipelines. Lead cost-performance governance, technical design reviews, AI standards, roadmap development, and mentoring. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while ensuring responsible AI deployment.

Designs, develops, deploys, and supports large-scale AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes AI infrastructure for scalability, latency, throughput, cost, and hardware utilization; defines reliability objectives; leads architecture and technical reviews; and mentors engineering staff.

Design, develop, deploy, and optimize foundational AI systems, including large language model inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture, cost-performance optimization, technical standards, and design reviews for scalable production AI systems. Mentor senior technical staff and contribute to the long-term roadmap for responsible AI infrastructure.

Conduct applied AI research and build foundation models from design through training, evaluation, validation, and production implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research areas include large language models, deep learning, optimization, self-supervised learning, robustness, explainability, reinforcement learning from human feedback, and model fine-tuning. Develop research libraries and platform code, publish findings, reproduce peer-reviewed work, and independently pursue impactful research agendas.

Conduct applied research and build AI foundation models, including large language and agentic AI systems, through design, training, evaluation, validation, and deployment. Partner with data scientists, engineers, and product managers to translate research into scalable customer-facing products. Lead research initiatives, investigate state-of-the-art methods, optimize large-model training and inference, and guide junior researchers or engineers.

Leads Capital One’s enterprise supplier management product strategy and greenfield third-party management platform. Responsibilities include developing business, product, pricing, marketing, credit risk, and analytics strategies; managing projects and programs; building requirements; leading cross-functional partnerships; mentoring teams; and driving executive-level decisions. Requires strong quantitative and qualitative analysis, people leadership, project management, product development, financial modeling, and strategic communication skills.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, AI standards, and design reviews while mentoring senior technical staff. Partner with research, engineering, product, and program teams to establish foundational AI roadmaps and deliver scalable, responsible systems.

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research LLM pretraining, fine-tuning, reinforcement learning, robustness, explainability, and behavioral models. Develop platform or solution-level code, reproduce research, document experiments, and contribute to technical reports or conference submissions.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and optimize foundation models through design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products, translate research into business outcomes, and publish technical findings. Requires advanced research expertise in deep learning, LLMs, optimization, fine-tuning, data preparation, and reproducible experimentation.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Optimizes large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads architecture and design reviews, establishes AI engineering standards, drives technical roadmaps, and mentors senior AI engineers.

Lead strategic product initiatives for Consumer and Developer Experience (CDX), perform consulting-style analysis and analytics, drive AI transformation workstreams, set long-term product vision, develop prototypes with engineering, and build cross-functional stakeholder relationships to align and execute strategic priorities.

Leads an AI engineering team building scalable, responsible generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and governance. Establishes technical strategy, optimizes production AI systems for cost and performance, manages cloud deployments, translates enterprise goals into roadmaps, and develops engineering talent.

Design, develop, optimize, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead optimization of production AI systems for scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, establish governance standards, lead design reviews, contribute to technical roadmaps, and mentor senior AI engineering staff.

Build and deploy responsible AI systems, including foundation model training, LLM inference, agents, similarity search, guardrails, evaluation, and observability. Optimize AI infrastructure for scalability, latency, throughput, cost, and hardware utilization. Develop deployment pipelines, testing environments, telemetry, prompts, evaluation datasets, and regression tests while contributing to foundational AI roadmaps and engineering standards.

Design, build, deploy, and support scalable AI systems, including foundation models, LLM inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor senior technical staff.

Design, develop, deploy, and support production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, lead AI governance and technical design reviews, establish engineering standards, and mentor senior technical staff.

Design, develop, deploy, and support foundational AI systems, including LLM training and inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define AI service-level objectives, lead technical reviews, ensure security and compliance, contribute to long-term roadmaps, and mentor engineering staff.