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

Conduct applied AI research and develop foundation models from design through training, evaluation, validation, and production deployment. Lead research initiatives spanning large-scale deep learning, language models, optimization, fine-tuning, robustness, and explainability. Partner with data scientists, engineers, and product managers to translate research into scalable AI products, while guiding junior researchers and advancing research agendas.

Lead an Applied AI data science team developing large-scale personalized recommendation and machine learning solutions. Partner with software engineers and product managers, build models through design, training, validation, and deployment, analyze massive structured and unstructured datasets, evaluate emerging technologies, and translate complex findings into business outcomes while developing team talent.

Leads data science for enterprise agentic AI products, developing and deploying machine learning models, designing experiments, analyzing telemetry, and translating findings into business strategy. Partners with data scientists, AI/ML engineers, and product managers while guiding large-scale analysis across numeric and textual data. The role also requires technical leadership, stakeholder communication, innovation, talent development, and expertise in statistical modeling, deep learning, and cloud-based data science.

Designs, develops, deploys, and optimizes enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, model routing, and observability. Establishes AI architecture standards, improves system cost and performance, champions responsible AI, and leads technical direction, mentorship, and cross-functional collaboration across Capital One.

Leads data science initiatives for customer financial resiliency, partnering with engineers and product managers. Builds, validates, implements, and productionizes supervised and reinforcement learning models across large-scale data. Uses Python, cloud platforms, open-source machine learning tools, and statistical methods to deliver personalized customer solutions. Translates complex analyses into business outcomes, evaluates emerging technologies, and develops team talent.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization. Define AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews and mentor engineering teams across complex AI product initiatives.

Build and operate an agentic workflow orchestration runtime for AI-generated marketing content. Develop generator and critic agents, parallel orchestration, bounded revision loops, LLM gateway integrations, rate limiting, backpressure, and observability. Lead technical projects and developers, deliver cloud-based microservices and full-stack solutions, mentor engineers, establish coding standards, and collaborate with product managers. The role requires software engineering, cloud, database, API, CI/CD, infrastructure, testing, and distributed-systems expertise.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, cost-performance governance, optimization, technical design reviews, and responsible AI standards. Partner with engineering, research, product, and program teams while mentoring senior technical staff and shaping the roadmap for foundational AI platforms.