Top AI & Machine Learning Jobs

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.

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 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.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models, optimize training and inference, develop scalable machine learning solutions, and translate research into production systems. Lead cross-functional research initiatives with data scientists, engineers, and product managers while independently pursuing impactful research agendas. The role emphasizes deep learning, NLP, model fine-tuning, robustness, explainability, and deployment at scale.

Develop and deploy large-scale machine learning and personalization models for web and mobile customer experiences. Partner with data scientists, engineers, and product managers; analyze massive numeric and textual datasets; and manage model design, training, evaluation, validation, and implementation. Research advanced methods including foundation models, reinforcement learning, causal inference, transformer architectures, and recommender systems while translating technical findings into business outcomes.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, foundation model training, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimizes AI systems for cost, latency, throughput, scalability, and hardware utilization. Owns complex AI architecture, defines reliability objectives, leads technical reviews, ensures security and compliance, contributes to long-term technical roadmaps, and mentors senior and principal engineers.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models, optimize training and inference, develop scalable AI solutions, and translate research into production systems. Lead cross-functional research initiatives with data scientists, software engineers, ML engineers, and product managers while pursuing impactful research and mentoring junior team members.

Build and lead scalable generative AI platforms, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, and hardware utilization; architect multi-model orchestration pipelines; establish technical standards and governance; and mentor senior AI engineers while shaping the long-term platform roadmap.

Develop and deploy high-scale machine learning and personalization models for Capital One’s web and mobile experiences. Responsibilities include researching recommendation systems, foundation models, reinforcement learning, causal inference, and transformer architectures; analyzing large datasets; building models through training, evaluation, validation, and implementation; and collaborating with data scientists, engineers, and product managers.

Build and optimize foundational AI systems, including LLM training and inference, agentic workflows, vector search, guardrails, evaluation, and observability. Partner with engineering, research, product, and program teams to deliver scalable AI products. Lead model and infrastructure optimization for cost, latency, throughput, and hardware utilization; establish governance and engineering standards; contribute to technical roadmaps; and mentor senior AI leaders.