Top AI & Machine Learning Jobs

Lead machine learning engineering for production systems at scale. Design and deploy models, data pipelines, cloud architectures, and ML infrastructure; write and test application code; automate deployment; monitor, retrain, and govern production models; and apply responsible AI practices. Collaborate with Product, Data Science, and Agile teams while using Python, Scala, or Java. The role also involves technical leadership and developing performant, resilient ML solutions.

Lead data science and machine learning initiatives for Capital One’s Global Payment Network. Build, validate, and implement models and AI tools supporting fraud, risk, merchant acceptance, intelligence, and monitoring. Collaborate with data scientists, engineers, and product managers while translating complex analytical work into business outcomes. Research emerging technologies, solve ambiguous problems, and develop team talent using Python, cloud platforms, open-source tools, and large-scale data.

Design, develop, test, deploy, and support production AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Develop state-of-the-art optimization techniques to improve scalability, cost, latency, throughput, and hardware utilization. Shape the long-term technical roadmap for foundational AI platforms while collaborating with engineers, research scientists, program managers, and product managers. Lead and mentor engineering teams and influence senior cross-functional stakeholders.

Lead and mentor applied research teams to design, train, evaluate, validate, and deploy large-scale AI foundation models. Partner with cross-functional product and engineering teams to deliver production AI platforms, perform high-impact applied research, scale training and inference, and represent Capital One in the research community.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Builds ML models, data pipelines, cloud architectures, and production applications; applies model tuning, validation, monitoring, governance, responsible AI, CI/CD, and test automation. Collaborates with Product and Data Science teams in Agile environments, reviews code, optimizes performance, and helps lead teams delivering resilient ML solutions.

Design, develop, deploy, and support scalable AI platform components, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for latency, throughput, cost, scalability, and hardware utilization. Contribute to foundational AI strategy, collaborate cross-functionally, and lead or mentor engineering teams.

The Applied Researcher II will work on developing AI foundation models, partner with cross-functional teams, and engage in impactful research to enhance customer experiences using advanced AI techniques.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Partner with engineers, scientists, technical program managers, and product managers while contributing to technical vision, roadmap development, and responsible AI practices. Lead and mentor engineering teams and apply emerging AI research to production systems.

Lead design, development, deployment, and support of large-scale AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, partner with cross-functional teams, and contribute technical vision and roadmap for foundational AI systems.

Lead design, development, deployment, and support of large-scale AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and optimization to improve scalability, cost, latency, and throughput while guiding technical vision and cross-functional delivery.