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

Design, develop, and deploy machine learning models for ad targeting, ranking, and bidding optimization. Build personalization and recommendation systems at scale. Lead incrementality testing, attribution, and causal inference efforts. Partner with product, engineering, and analytics to deliver low-latency, production-ready ML pipelines and drive experimentation and measurement strategies across the AdTech ecosystem.

Lead design, procurement, and scaling of Mastercard's on‑premise AI infrastructure. Define standards for compute, storage, networking, cooling and data center design; run RFI/RFPs; partner with data science, MLOps, platform, security, and governance teams to support large‑scale training and low‑latency inference for generative and predictive AI workloads.

Develop and advance Mastercard’s global AI policy positions, narratives, and advocacy strategies. Advise internal executives, business units, government affairs teams, regulators, and standards bodies on AI legislation, regulation, standards, governance, and deployment risks. Translate technical AI concepts and business objectives into policy frameworks, briefs, consultations, and executive materials. Monitor global developments, support thought leadership, engage standards organizations, and coordinate cross-functional responses across legal, regulatory, privacy, cybersecurity, and Services teams.

Develop and advance Mastercard’s global AI policy positions, narratives, and advocacy strategies. Advise internal executives, business units, government affairs teams, regulators, and standards bodies on AI legislation, regulation, governance, standards, and emerging risks. Translate AI business objectives into policy frameworks, produce policy briefs and executive materials, support consultations and thought leadership, and promote consistent, innovation-enabling AI policies across global markets.

Lead the design and implementation of agentic AI and LLM applications within Mastercard's Operational Intelligence team, focusing on backend and production-scale solutions. Mentor engineers and ensure high reliability and observability of systems. Develop integration between AI platforms and enterprise data services.

Lead design, procurement, and lifecycle management of on-prem AI infrastructure. Act as product owner translating data science needs into scalable compute, storage, networking, and accelerator platforms. Drive RFI/RFPs, architect high-performance systems for large-scale training and low-latency inference, define infrastructure standards, partner with MLOps/data science/security/governance, influence executives, and evaluate emerging AI infrastructure technologies.

Lead embedded AI transformation by partnering with business units to identify high-value use cases, design and deploy production-grade AI agents and assistants, and train teams. Build repeatable architectures, playbooks, and training to scale AI adoption, measure business impact, and develop internal champions.

Lead product strategy and execution for an enterprise AI platform (Agent Factory) to enable design, build, evaluation, deployment, monitoring, governance, and commercialization of AI agents. Manage a small product team, partner with engineering, security, legal and business stakeholders, embed responsible AI and governance controls, drive adoption and metrics, and prioritize platform investments to scale trusted, production-ready agents.

Embed with business units to identify high-value AI use cases, design and deploy production-ready AI assistants and multi-agent solutions, and coach teams to adopt AI-first workflows. Deliver measurable business outcomes, create reusable playbooks and training, and scale organizational capability through hands-on engagements and knowledge capture.