Top AI & Machine Learning Jobs in New York City, NY

The role involves leveraging advanced machine learning techniques to analyze cash flow and transaction data, developing predictive models, and collaborating with a cross-functional team to drive customer-focused solutions.

Lead development and deployment of generative AI solutions for financial data analysis. Fine-tune multimodal LLMs, manage large-scale data annotation, build and maintain model/data pipelines with engineers, leverage Python/PyTorch/HuggingFace/Dask/LangChain, and translate technical work into business outcomes.

As a Senior Machine Learning Engineer at Capital One, you'll design, build, and deploy ML models, collaborate in Agile teams, and ensure the performance of ML applications. Responsibilities include model training, monitoring, and optimization in cloud-based environments.

The role involves partnering with teams, leveraging technologies to analyze data, building machine learning models, and translating complex work into business outcomes.

The Senior Lead AI Engineer will develop and support AI software components, lead cross-functional teams, and optimize AI solutions for Capital One's banking systems.

Lead a team of data scientists to develop AI/ML solutions, leveraging advanced technologies for customer interactions and product innovations. Build and implement NLP models for large-scale applications while collaborating with cross-functional teams.

As a Principal Data Scientist, you'll innovate and develop machine learning models, analyze vast data, and work with cross-functional teams to deliver valuable customer products.

Manage data science initiatives to develop machine learning models and collaborate with cross-functional teams for data-driven solutions, focusing on customer-centric innovation.

As a Senior Manager, Data Scientist - Applied AI, you'll lead a team to develop AI-powered financial products, utilizing machine learning and data analysis to enhance customer interactions with Capital One.

Lead design, build, deploy, and support production AI systems (foundation model training, LLM inference, vector search, guardrails, evaluation, observability). Optimize large-scale model performance and collaborate with cross-functional teams to define technical vision and roadmap.