Top AI & Machine Learning Jobs

Leads development of scalable AI/ML platforms and developer experiences. Designs APIs, SDKs, libraries, microservices, and tools; integrates GenAI capabilities including LLMs, RAG, agents, orchestration, inference, and evaluation. Builds observability, monitoring, safety, and responsible AI capabilities using cloud, distributed systems, databases, containers, and Kubernetes. Partners across product, design, data science, and engineering teams, supports platform users, explores emerging technologies, and mentors engineers.

Develop machine learning models supporting credit underwriting and business card strategies. Partner with data scientists, software engineers, product managers, and business stakeholders to design, train, validate, evaluate, backtest, and implement models. Analyze large numeric and textual datasets using Python, AWS, Spark, H2O, and related technologies, translating complex findings into business outcomes.

Design, develop, test, deploy, and support production AI systems, including foundation-model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI training and inference for scalability, latency, throughput, hardware utilization, and cost. Shape the technical vision and roadmap for foundational AI platforms while partnering with engineers, researchers, program managers, and product leaders. Mentor engineering teams and influence senior cross-functional stakeholders.

Designs, develops, tests, deploys, and supports foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develops optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost. Partners with engineering, research, product, and program teams; contributes to technical vision and roadmaps; and mentors teams while applying emerging AI research to production systems.

Conduct applied AI research and build foundation models from design through training, evaluation, validation, and production implementation. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Research state-of-the-art methods, optimize large deep learning models, translate research into business outcomes, and independently pursue impactful research agendas. Work may involve language, image, event, or graph models, including self-supervised learning, robustness, explainability, and reinforcement learning from human feedback.

Leads the design, development, deployment, and operation of scalable machine learning systems. Builds ML models, data pipelines, cloud architectures, and production applications; optimizes training and performance; automates testing and deployment; monitors and retrains models; and applies responsible AI, explainability, governance, and security practices. Collaborates with Product, Data Science, and Agile engineering teams while providing technical and potentially people leadership.

Build and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to technical strategy and roadmap, collaborate cross-functionally, and lead or mentor engineering teams while applying emerging AI research in production.

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.

Develop machine learning and statistical models across large numeric and textual datasets, from design through implementation. Collaborate with data scientists, software engineers, and product managers to deliver customer-focused products. Analyze data, validate and backtest models, apply techniques such as classification, clustering, sentiment analysis, time series, and deep learning, and translate technical findings into business goals.

Build and lead development of an enterprise AI platform supporting large-scale AI-assisted analysis. Responsibilities include designing, developing, testing, deploying, and supporting foundation model training, LLM inference, similarity search, guardrails, evaluation, experimentation, governance, and observability. The role also requires advancing LLM optimization for scalability, latency, throughput, hardware utilization, and cost while shaping the long-term roadmap for foundational AI systems and mentoring engineering teams.