Top Machine Learning Engineer Jobs in NYC, NY

The Staff ML Engineer will develop foundation models and AI Assistants, driving technical excellence and collaborating with engineering teams to enhance Stripe's ML capabilities.

Adobe seeks a Machine Learning Engineer for applying AI techniques to enhance customer experience through predictive models and analytics. Responsibilities include developing scalable algorithms, collaborating on product features, and solving complex business problems using statistical and machine learning methods.

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.

The ML Engineer will build, deploy, and iterate on ML systems, focusing on classification, human feedback loops, and working with real-world data.

The Staff Machine Learning Engineer will define technical strategies, design ML models for document processing and risk assessment, and build scalable ML systems to protect Coinbase users' funds.

The Staff Machine Learning Engineer will utilize AI models, mentor team members, and enhance automation solutions across Coinbase's operations, focusing on machine learning to solve complex challenges.

Lead the design and implementation of AI/ML models for risk and fraud detection at Coinbase. Collaborate with teams to develop scalable systems, mentor engineers, and ensure effective AI/ML solutions align with company goals.

As a Senior Machine Learning Engineer, you will design, build, and deploy large-scale ML systems to enhance Dropbox's AI features, collaborating with teams and mentoring peers.

Machine Learning Engineers at Tennr will architect, train, deploy models, optimize data pipelines, integrate ML into backend systems, and collaborate with teams for product evaluation.

Design, build, and scale core infrastructure for machine learning models, manage production systems, and implement best practices in MLOps.

The Machine Learning Engineer will develop the internal ML environment, partnering with research to ensure models are production-ready and optimizing infrastructure performance.