Top Digital Media Machine Learning Engineer Jobs in New York City, NY

The Lead Machine Learning Engineer will manage AI/ML project lifecycles, collaborate with teams on model optimizations, implement best practices in MLOps, and mentor juniors while ensuring model reliability.

As an ML Engineer Intern, you will build and deploy ML models, design scalable infrastructure, and collaborate with data science teams in a fast-paced environment.

Develop and implement machine learning algorithms, optimize statistical models for advertising measurement, ensure data integrity, and communicate insights to stakeholders.

As a Senior Machine Learning Engineer, you will develop high-performance services, innovate AI/ML applications, design algorithms, and collaborate closely with teams to optimize ad systems.

Individual-contributor ML engineer who designs, prototypes, and ships AI-powered newsroom and reader-facing tools. Build and deploy production systems leveraging LLM APIs and web technologies, collaborate with journalists and cross-functional teams, create evaluation frameworks, ensure responsible AI use, and iterate on MVPs based on newsroom feedback.

As a Machine Learning Engineer, you will develop and deploy machine learning models, working closely with AI experts to innovate and improve generative AI applications.

The role involves developing advanced machine learning and deep learning models for personalization and user modeling in large-scale systems, collaborating with teams, and driving business impact through innovative algorithms.

The Senior Data Engineer will develop and deploy machine learning models, build end-to-end ML pipelines, optimize performance, and collaborate across teams to enhance AI capabilities at ClickUp.

Design, iterate, and productionize ML models for recommendation, ranking, routing, and monetization. Lead A/B testing and causal measurement, build dashboards and analytics, define metrics, collaborate with Product and Engineering, and scale ML/analytics infrastructure and model lifecycle management.

The role involves owning and evolving production ML systems, developing models, collaborating with stakeholders, and ensuring efficient system performance through coding and infrastructure contributions.