Top Hybrid Machine Learning Engineer Jobs in New York City, NY

The role involves designing and implementing AI/ML-driven capacity forecasting models, collaborating with cross-functional teams, and optimizing resource demand across infrastructure environments.

The role involves building machine learning models to detect and reduce fraud in Block's Cash App and Square ecosystems. Responsibilities include collaboration with cross-functional teams, developing ML-driven solutions, and improving the ML lifecycle efficiency, while also mentoring teammates.

As a Principal Associate Data Scientist, you'll develop machine learning models for credit card customer acquisitions, leveraging vast datasets and working collaboratively with cross-functional teams to deliver impactful solutions.

The Principal AI/ML Engineer will lead the design and implementation of machine learning solutions in AdTech, focusing on campaign optimization and user personalization while also mentoring other engineers.

The role involves designing and deploying machine learning models, optimizing large language models, and collaborating with cross-functional teams to enhance data management for alternative investments.

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.

Develop and deploy machine learning applications, collaborating with teams to solve business challenges. Create ML solutions, analyze data, and communicate results.

The role involves designing and deploying ML models for domain search, including ranking and personalization, while collaborating with cross-functional teams.

The Machine Learning Engineer will build and deploy data products, collaborate cross-functionally, improve customer experiences, and mentor others in AI methods.

The Staff Machine Learning Engineer will architect and optimize data infrastructure for multimodal AI models in oncology, focusing on data management and processing pipelines.