Top Artificial Intelligence Machine Learning Engineer Jobs in New York City, NY

Develop advanced algorithms, apply machine learning, collaborate with clients, and assess new datasets and technologies for data science projects.

Design, build, and deploy scalable AI and GenAI solutions by wrangling data, developing ML models, and maintaining data infrastructure. Collaborate with clients, perform complex analyses, apply NLP and deep learning techniques, and mentor junior team members to deliver production-ready AI systems.

Lead the design and development of Datadog's experimentation engine, translating complex statistical methods into production systems while collaborating with cross-functional teams and educating stakeholders on best practices.

Lead design, development, and deployment of enterprise AI agents and multi-agent systems. Architect scalable, secure AI/data systems, integrate vector DBs, embeddings, RAG, and cloud MLOps, drive automation/APIs, mentor engineers, set KPIs, and ensure observability, ethical AI, and production readiness.

Develop and maintain machine learning models for user safety, design scalable systems, and collaborate on AI solutions while ensuring fairness. Requires strong programming and domain expertise in ML technologies.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

The AI/ML Engineer will design, build, and deploy machine learning systems, optimize workloads, and ensure scalability and compliance of the ITMS platform.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

The role involves developing AI and ML solutions, collaborating with teams to enhance client experiences, and ensuring proper model lifecycle management.

Lead the development of large-scale recommendation systems at Snap, overseeing technical leadership, collaboration across teams, and implementation of machine learning strategies to enhance content discovery and personalization.