Top AI Jobs in NYC, NY

The Applied AI Engineer designs and deploys AI-powered solutions, collaborating with teams to automate workflows and improve efficiency across Alloy.

As a Principal AI/ML Engineer, you will lead the development of machine learning solutions for advertising, focusing on optimization, personalization, and deploying models within a highly scalable infrastructure.

The Enterprise Architect will design scalable AI solutions, mentor teams, support business development, and manage client relationships while evolving technology practices.

As a Senior Lead AI Engineer, you will design and develop advanced AI systems, collaborate with cross-functional teams, and introduce innovative AI techniques while mentoring others.

The Senior Lead AI Engineer will collaborate with cross-functional teams to design, develop, and implement AI software solutions, focusing on large-scale production systems and AI model optimization.

As an AI Product Engineer, you'll build and scale AI-powered features for Notion's Custom Agents, collaborating with teams to enhance user experience and implementing cutting-edge technologies.

As a Lead AI Engineer, you will develop, deploy, and support AI software components, collaborate with cross-functional teams, and optimize performance of large-scale AI systems.

The Lead AI Engineer will design, develop, deploy, and support AI software components, collaborating with cross-functional teams to create scalable AI solutions that enhance customer interactions and operational efficiency.

The Lead AI Engineer will develop and deploy AI software components, optimize AI systems, and collaborate with cross-functional teams at Capital One to enhance customer interactions through AI technology.

As a Senior Lead AI Solutions Consultant, you will drive revenue through the adoption of BrazeAI tools, managing technical discussions, running proofs-of-concept, and educating clients on AI concepts.

The Director, AI & Advanced Data Learning oversees the learning strategy for advanced AI and data roles, ensuring alignment with Mastercard's technology needs. This role focuses on skill development, designing practical learning experiences, and measuring the impact on technical teams' performance.

Lead design, development, and operation of production-grade agentic AI systems (multi-agent, multi-tool) to automate Services processes. Define safe tool-use patterns, observability, evaluation metrics, and drive cross-functional adoption and operational readiness.

Lead AI infrastructure strategy, design, and delivery for Mastercard's private cloud. Collaborate cross-functionally to ensure scalable, performance-driven solutions for AI workloads.

Design, develop, deploy, and support foundational AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize training/inference for scalability, cost, latency, and throughput. Partner cross-functionally to deliver production AI services and shape the technical vision and roadmap for foundational AI at scale.

Lead design, develop, test, deploy, and support foundational AI systems including foundation model training, LLM inference, similarity search/VectorDBs, guardrails, evaluation, observability, and optimization for scalability, cost, latency, and throughput. Partner cross-functionally and contribute to technical vision and roadmap.

Lead vision, strategy, and delivery of AI across Pfizer's U.S. commercial organization. Own a prioritized portfolio from concept to production and adoption, build and lead multidisciplinary teams, manage vendors and budgets, ensure shared platforms, standards, governance, and responsible AI, and influence senior leaders to drive enterprise-scale transformation and measurable business value.

Perform exploratory and descriptive analysis, statistical modeling, predictive modeling and machine learning to generate insights. Build data visualizations and data pipelines, ensure data quality, collaborate with stakeholders, and translate results into client recommendations while upholding professional standards and developing leadership skills.

Lead implementation of software quality standards, design and run functional, compatibility, and performance tests, identify and resolve defects, manage release QA and security testing, collaborate with cross-functional teams, mentor junior staff, and use data-driven insights to improve product quality and processes.

The role involves designing and implementing AI and machine learning solutions, transforming data into insights, and supporting client engagements. Responsibilities include model development, data analysis, and enhancing model performance using established technologies.

This role involves managing AI and data product strategy, requirements, and roadmaps, partnering with engineering and business teams to enhance AI solutions.

As an Applied Researcher, you will collaborate with teams to develop AI products, leveraging technologies to enhance customer experiences through research and model implementation.

The Senior Lead AI Engineer will design, develop, and support AI systems, leveraging machine learning technologies to enhance customer experiences and optimize AI performance.

Lead design, development, and deployment of scalable, ethical AI agents and multi-agent systems. Architect integrations, automation workflows, and MLOps pipelines on cloud platforms. Drive technical strategy, mentor engineers, define KPIs, ensure observability and operational excellence, and collaborate across product, data, and UX to deliver enterprise AI solutions.

Lead go-to-market and technical strategy for AI developer tooling, winning early customers and building repeatable playbooks. Translate customer MLOps requirements into product feedback, partner with engineering and Weights & Biases, and enable sales/solutions teams to close deals around experiment tracking, model governance, and LLM observability.

Build and deploy transformer and sequential deep learning models using large-scale financial and behavioral data to power personalized growth and marketing experiences. Work cross-functionally to design scalable training, serving, and monitoring infrastructure (batch and real-time), contribute to experimentation and optimization frameworks, and maintain production-grade MLOps systems to improve member engagement and business metrics.