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

Lead a product team building ML and GenAI infrastructure and model delivery for marketing and acquisitions. Define problem statements, set 3-year roadmaps tied to business outcomes, partner with data scientists and engineers, drive reusable scalable capabilities, influence cross-functional stakeholders, recruit and mentor talent, and measure success with data-driven metrics.

Lead product efforts for machine learning-driven marketing and acquisition products. Collaborate with data scientists, engineers, and designers to define acceptance criteria, break down epics into MVP features and user stories, prioritize roadmaps, manage agile delivery, and drive stakeholder alignment to build scalable ML product platforms.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

As a Senior Manager, Data Scientist, you will lead a team to develop generative AI solutions and build machine learning models, enhancing customer experiences and operational efficiency.

Lead a cross-functional team to deliver AI-powered banking products, focusing on developing and supporting AI software components and optimizing LLM technologies.

The Senior Lead AI Engineer at Capital One will design, deploy, and support AI systems, working with cross-functional teams to innovate and enhance customer interactions while ensuring scalability and performance.

Lead design, development, deployment, and support of production-scale AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability). Drive LLM optimizations, technical vision, and cross-functional delivery of responsible AI products.

Design and build an enterprise generative AI platform: agent orchestration, memory, guardrails, vector search, SDKs, and blueprints. Standardize agentic workflows, optimize orchestration and cost, own Helm/Kubernetes operators, ensure trust and safety, mentor engineering teams, and evangelize platform architecture internally and externally.

The role involves leveraging technologies like Python and AWS to develop statistical and machine learning models, translating complex data insights into business goals, and working with a cross-functional team.