Top AI & Machine Learning Jobs

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to platform roadmaps, and mentor engineering teams.

Design, develop, deploy, optimize, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, design reviews, and technical roadmaps for scalable production AI. Optimize model performance, hardware utilization, latency, throughput, and cost while ensuring responsible AI standards. Mentor senior technical staff and collaborate with engineers, researchers, program managers, and product managers.

Design, build, deploy, and support large-scale responsible AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluations, observability, and agentic applications. Develop optimization techniques for latency, throughput, scalability, hardware utilization, and cost. Partner with engineering, research, product, and business teams; mentor senior technical staff; author technical content; and represent Capital One at leading AI conferences.

Design, develop, deploy, optimize, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance optimization across scalable AI platforms, establish engineering standards, contribute to long-term technical roadmaps, and mentor senior AI engineers.

Conduct applied AI research and lead development of foundation models from design through deployment. Partner with data scientists, engineers, and product managers to deliver scalable AI products. Research large deep learning models, optimization, self-supervised learning, robustness, explainability, and reinforcement learning from human feedback. Lead research initiatives, translate findings into production systems, and guide junior researchers or engineers.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and multi-model orchestration. Lead cost-performance governance, technical design reviews, AI engineering standards, and long-term platform strategy. Apply state-of-the-art optimization techniques to improve model scalability, latency, throughput, hardware utilization, and cost. Mentor senior technical leaders and collaborate with engineers, scientists, product managers, and program managers.

Leads a small data science team and cross-functional initiatives transforming bank claims management through machine learning and agentic workflows. Responsibilities include data exploration, model development, evaluation, validation, deployment, roadmap development, stakeholder influence, and establishing datasets, tools, and evaluation protocols for enterprise AI. The role remains hands-on while providing strategic oversight and developing team talent.

Lead data science initiatives using statistical modeling, machine learning, and large-scale cloud computing. Partner with software engineers and product managers, develop and deploy models from design through implementation, analyze petabyte-scale data, and communicate insights to business stakeholders. The role requires expertise in open-source tools, relational databases, machine learning, MLOps, deep learning, distributed processing, and production-grade ML systems.

Leads data science initiatives for commercial banking by developing and validating machine learning models, conducting statistical analysis and back-testing, modernizing data infrastructure, and building analytical tools for risk, underwriting, portfolio management, and client-focused products.

Lead a data science team developing scalable AI and NLP solutions for customer-facing financial applications. Partner with data scientists, engineers, product managers, and designers to build, train, evaluate, fine-tune, and operationalize machine learning and large language models. Drive research into generative AI, training optimization, self-supervised learning, explainability, and RLHF while delivering production systems at scale. Translate technical findings into business outcomes and develop team talent.