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

Lead design, build, deploy, and support scalable, responsible AI systems (foundation model training, LLM inference, similarity search, guardrails, observability). Collaborate with cross-functional teams, optimize model performance and cost, and shape technical vision and roadmap for foundational AI platform services.

Design, build, deploy, and maintain production-scale ML models and pipelines. Collaborate with product and data science teams to architect ML systems, implement model training and monitoring, optimize performance and reliability, and apply Responsible/Explainable AI practices using cloud and distributed computing technologies.

Lead an AI engineering team to design, build, deploy, and support foundation-model and LLM-based systems. Oversee model training, inference, similarity search, guardrails, evaluation, observability, and scalability optimizations. Make build-vs-buy decisions across OSS and SaaS AI technologies, set technical vision and roadmap, and recruit, mentor, and retain engineering talent.

Lead design and build of an enterprise AI content platform: RAG-backed knowledge library, embedding search pipeline, prompt MLOps, validation/critic pipelines, shared platform APIs, architecture and mentorship for production LLM systems.

Lead design, develop, deploy, and support large-scale AI systems (foundation model training, LLM inference, vector search, guardrails, observability). Partner cross-functionally to optimize performance, scalability, and cost, and shape long-term foundational AI technical vision and roadmap.

The role involves leading AI product development, working with cross-functional teams to enhance AI systems, and optimizing large-scale production AI systems.

As a Manager in Data Science, you'll lead the development of machine learning models, partner with engineering and product teams, and analyze large datasets to enhance customer experiences and prevent fraud.

Owner of product features for Card Intelligence ML initiatives; collaborate with data scientists, engineers, and stakeholders to define roadmaps, break down epics into MVPs, prioritize features, and accelerate model-to-market delivery for fraud prevention.

As an Applied Researcher II, you'll develop AI models, collaborate across teams, conduct applied research, and drive innovations in AI to improve customer experiences.

As a Data Scientist, you'll analyze complex datasets to optimize fraud authentication, design ML solutions, and collaborate with cross-functional teams.