Jobs at Capital One

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, software engineers, machine learning engineers, and product managers to deliver scalable AI products. Research advanced methods involving language, vision, graphs, optimization, robustness, explainability, and reinforcement learning. Produce technical reports and conference submissions while independently pursuing impactful research agendas and delivering models, libraries, and platform code to production.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, and observability. Lead multi-model orchestration, cost-performance governance, technical design reviews, AI standards, and long-term platform strategy. Mentor senior AI engineers while applying advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost efficiency across responsible production AI systems.

Build and lead production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, orchestration, evaluation, governance, and observability. Optimize AI platforms for cost, latency, throughput, scalability, and hardware utilization. Define technical roadmaps and standards, lead design reviews, govern GPU and inference efficiency, integrate heterogeneous AI systems, and mentor senior technical leaders while applying emerging research responsibly.

Design, develop, deploy, and optimize foundational AI systems, including large language model inference, agentic workflows, vector search, model evaluation, governance, and observability. Lead optimization of production AI systems for cost, latency, throughput, and scalability. Define technical roadmaps and standards, conduct governance reviews, architect multi-model orchestration pipelines, lead design councils, and mentor senior AI engineering personnel.

Conduct applied AI research across foundation models, large language models, fine-tuning, reinforcement learning, and behavioral models. Build, train, evaluate, validate, and deploy scalable AI systems using modern open-source frameworks and cloud infrastructure. Collaborate with data scientists, engineers, and product managers to deliver AI-powered products, translate research into business outcomes, and contribute to technical reports or conference publications. Own research agendas, develop novel methods, and deliver production-scale model and platform solutions.

Build and optimize foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance optimization for scalable production AI platforms, integrate heterogeneous models, establish engineering standards, and mentor senior technical staff. Collaborate with research, engineering, product, and program teams to deliver responsible AI capabilities.

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

Conduct applied AI research and develop foundation models from design through deployment. Partner with data scientists, engineers, and product managers to deliver scalable AI products. Lead research initiatives involving large-scale deep learning, model optimization, self-supervised learning, robustness, explainability, fine-tuning, and data preparation. Translate research findings into production systems, guide junior team members, and independently pursue impactful research agendas.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead multi-model orchestration and cost-performance optimization across production AI platforms. Establish engineering standards, lead design reviews, contribute to technical roadmaps, and mentor senior AI engineering leaders. Collaborate with research, product, engineering, and program management teams to deliver scalable and responsible AI capabilities.

Build and deploy foundational AI systems, including foundation model training, LLM 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 architectures, define AI service-level objectives, lead technical reviews, ensure security and compliance, contribute to long-term technical roadmaps, and mentor engineering staff.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, latency, throughput, cost, and hardware utilization across production platforms. Own complex AI architecture, reliability objectives, security, and compliance reviews while contributing to technical roadmaps and mentoring engineering colleagues.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, guardrails, model evaluation, similarity search, governance, and observability. Optimize large-scale model training and inference for cost, latency, throughput, and hardware utilization. Define AI reliability objectives, lead technical reviews, ensure security and compliance, shape platform roadmaps, and mentor engineering associates.

Design, build, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Define reliability objectives, lead architecture and technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering associates.

Design, develop, deploy, and optimize responsible AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead orchestration and cost-performance governance for large-scale production AI platforms, establish engineering standards, contribute to technical roadmaps, and mentor senior AI engineers. Collaborate with research, engineering, product, and program teams to deliver scalable AI capabilities.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models throughout the development lifecycle, optimize training and inference, and translate research into scalable production systems. Collaborate with data scientists, software and ML engineers, and product managers to deliver AI-powered customer experiences. Lead research initiatives, establish research agendas, publish high-quality work, and mentor junior researchers or engineers.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models across design, training, validation, and deployment. Lead research initiatives, develop scalable model and platform solutions, collaborate with data science, engineering, and product teams, and translate research findings into production AI products. Required expertise includes deep learning, model optimization, research agenda ownership, and delivering high-volume inference systems.

Develop and deploy foundational AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish governance standards, lead technical reviews, contribute to long-term AI strategy, and mentor senior technical staff while applying responsible AI practices.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization across production systems. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, shape foundational AI roadmaps, and mentor engineering staff.

Conduct applied AI research and develop foundation models across design, training, evaluation, validation, and production implementation. Collaborate with data scientists, engineers, machine learning specialists, and product managers to deliver scalable AI products. Research emerging methods, optimize large deep learning systems, publish findings, prepare technical reports, and independently pursue impactful research agendas.

Build and deploy foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize large-scale AI systems for cost, latency, throughput, and hardware utilization. Lead architecture and governance reviews, define long-term technical roadmaps, integrate heterogeneous AI components, enforce responsible AI standards, and mentor senior technical staff.