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

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Design, build, and scale production ML systems (including LLMs) to automate customer support and compliance workflows. Lead technical roadmaps, write high-quality Python code, mentor engineers, ensure secure, scalable architectures, and resolve complex reliability issues.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Design and maintain scalable ML models for logistics. Own end-to-end projects and collaborate with teams to deliver impactful customer features.

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 Senior Machine Learning Engineer will modernize Unity's AI systems by designing intelligent agents, developing applications, and ensuring quality through collaboration and innovation.

The intern will assist in training and evaluating machine learning models, using AI tools for development, and providing feedback on features, while validating model performance and usability.

As an ML Engineer, you will develop and deploy machine learning models, collaborate on research, and mentor team members in materials science applications.

The Machine Learning Engineer will design, build, and ship production-grade AI systems, collaborating with teams to optimize workflows and evaluate AI results.

The Research Engineer/Scientist will advance decision-making in critical industries using optimization and machine learning, collaborating with product managers and engineers.

Design, build, and deploy AI features for claim synthesis; implement machine learning solutions and improve model performance through collaboration and evaluation.

Design and implement AI-driven features for claims synthesis, leveraging LLMs and statistical techniques, while ensuring system performance and accuracy.