Top Machine Learning Engineer Jobs in NYC, NY

The Applied AI/ML Engineer will design, develop, and deploy AI and machine learning solutions while collaborating with cross-functional teams to optimize processes and enhance operational efficiency.

As a Machine Learning Engineer, you will analyze complex datasets, develop models, manage ML Ops, and enhance customer products using machine learning techniques.

Develop and deploy machine learning models for Hinge's dating platform, collaborating with teams and mentoring junior engineers.

The Staff Machine Learning Engineer will design, develop, and deploy ML systems to enhance user growth and monetization at Hinge, collaborating with teams and ensuring ethical AI practices.

Develop and productionize a next-gen Dispatch System, build ML models, automate processes, and collaborate with various teams while mentoring other engineers.

Responsible for designing and developing AI/ML product features and infrastructure to empower intelligent business decisions and operational excellence for customers.

The role involves building computer vision models, researching deep learning methods, handling large datasets, and implementing MLOps best practices.

Dandy seeks a Machine Learning Engineer to develop 3D computer vision models using SOTA methods, collaborate with product teams, and implement MLOps pipelines.

Lead the design, development, and implementation of machine learning applications, focusing on architecture, model training, and deployment using emerging technologies.

The role involves deploying and scaling machine learning models, optimizing systems for ML applications, and collaborating with various teams to support data science functionalities at Squarespace.

The AI/ML Platform Engineer will design and scale AI and ML infrastructure, collaborating with teams to deploy machine learning models and enhance user experience across Whatnot's marketplace.

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.

As a Machine Learning Engineer, you will leverage LLMs to improve language understanding, design ML models, and collaborate on innovative features.

The role involves developing and optimizing machine learning models, backend Python development, and managing ETL processes within a collaborative Agile team.

Design, build, and deploy machine learning and generative AI systems, transforming prototypes into production systems while collaborating with technical teams.

Responsible for designing and developing AI/ML product features and customer solutions, collaborating with various teams to enhance Celonis products.

Design, build, and deliver machine learning models and components. Ensure performance and monitor models in production as part of an Agile team.

The role involves designing, developing, and supporting AI and ML products, collaborating with teams to transform risk management and deliver value to customers through technology.

Lead the design, development, and integration of AI/ML architectures and systems, mentor junior engineers, and ensure compliance with industry standards.

The Applied ML Engineer at Ideogram will turn generative models into production features, collaborate with teams to define metrics, and ensure ML systems' reliability.

Lead the development and architectural design of AI and machine learning solutions, ensuring best practices in SDLC, model optimization, and team mentorship while managing end-to-end model lifecycle.

The Machine Learning Engineer will develop intelligent features to enhance code security using LLMs, collaborate with researchers, and manage ML models on AWS.

Develop advanced ML solutions to improve Stripe's payment products. Work on the ML lifecycle from research to deployment, collaborating with product teams.

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

Develop and implement machine learning models for evaluating disputes and chargebacks, and create a model orchestration layer. Collaborate on data use for enhanced user experiences.