Top AI & Machine Learning Jobs in New York City, NY

Leads enterprise AI engineering strategy and multi-team execution. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and production optimization. Makes technology decisions, scales reusable AI infrastructure and responsible AI practices, partners with research and risk teams, and develops engineering talent. Requires strong hands-on expertise in AI systems, cloud deployment, model optimization, and organizational leadership.

Conduct applied AI research and build foundation models from design through production deployment. Partner with data scientists, engineers, and product managers to develop scalable models, optimize training and inference, evaluate emerging methods, and translate research into customer-facing banking products. Lead research initiatives, define research agendas, mentor colleagues, and deliver production-scale solutions involving language, graph, image, or sequential data.

Build, deploy, scale, and monitor machine learning models and production services. Responsibilities include model development, feature selection, data pipeline construction, distributed data processing, cloud and Kubernetes operations, CI/CD automation, testing, model governance, responsible AI, and collaboration with product and data science teams.

Lead a team to analyze data, create machine learning models, and deliver insights for customer-focused financial products, leveraging various technologies.

Leads data science for Capital One’s US Card business, developing and deploying machine learning models across marketing, acquisitions, underwriting, and fraud prevention. Partners with data scientists, engineers, and product managers; analyzes large-scale numeric and textual data using cloud and open-source technologies; translates complex insights into business outcomes; and develops talent while guiding technical innovation.

Leads the Bureau Data Strategy team in extracting insights from credit bureau data and shaping underwriting data usage. Partners with data scientists, engineers, and product managers; develops machine learning models from design through implementation; analyzes large-scale numeric and textual datasets; translates technical findings into business outcomes; and develops team talent.

Build and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Own complex architectures, reliability SLOs, security and compliance reviews, technical roadmaps, and cross-functional delivery while mentoring engineering teams.

Lead data science for Capital One’s US Card organization by building and deploying machine learning models across the credit card lifecycle. Partner with software engineers and product managers, analyze large-scale customer data using Python, cloud, and distributed computing technologies, and translate statistical insights into business outcomes. The role also emphasizes innovation, stakeholder communication, technical leadership, talent development, and managing complex data science initiatives.

Develop machine learning and statistical models across the full lifecycle, including design, training, evaluation, validation, and implementation. Analyze large-scale numeric and textual datasets using Python, cloud computing, and distributed data technologies. Collaborate with data scientists, software engineers, and product managers to translate complex analyses into customer-focused business outcomes. Apply methods including classification, clustering, sentiment analysis, time series, and deep learning.

Designs, builds, deploys, and maintains production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI, governance, and security practices. The role collaborates with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.