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

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization; define reliability objectives; own complex system architecture; lead technical reviews; ensure security and compliance; and mentor engineering associates.

Conduct applied AI research and lead development of foundation models from design through training, evaluation, validation, and deployment. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Advance research in areas such as large language models, optimization, fine-tuning, robustness, and explainability while translating findings into production systems and business impact.

As a Senior Data Scientist at Capital One, you will lead AI-powered product development using machine learning technologies, focusing on NLP and large language models to enhance customer experiences.

Conduct applied research in AI safety and multimodal systems, developing foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver AI products at scale. Research areas include large language models, optimization, self-supervised learning, robustness, explainability, red teaming, safety guardrails, and multimodal vulnerability assessment. Publish findings, reproduce research, and independently pursue impactful research agendas.

Develop and deploy scalable optimization, simulation, and machine learning models for banking decision systems. Translate complex business challenges into mathematical problems, build production-grade algorithms using Python and optimization solvers, design experiments, and integrate modeling solutions with software systems. Collaborate with Product, Engineering, and Business teams to improve customer experiences, risk policies, and operational decision-making.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for cost, latency, throughput, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead technical reviews, and mentor senior AI engineers.

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research emerging methods, develop model and platform code, translate technical findings into business outcomes, and contribute to internal reports and conference publications. The role may focus on large language models, training and inference optimization, or fine-tuning.

Lead data science and model risk initiatives by partnering with data scientists, engineers, and product managers. Build, evaluate, validate, and implement machine learning models using large-scale numeric and textual data. Apply statistical methods, backtesting, and model-risk expertise while translating complex analyses into business outcomes. Responsibilities also include researching emerging technologies, improving decision-making, and developing talent.

Conduct research and develop machine learning solutions involving embeddings, foundation models, transformer models, and customer behavioral analytics. Build, evaluate, validate, and operationalize models using large-scale transaction and textual data. Partner with engineering, product, and business teams to improve customer experiences and outcomes across marketing, servicing, and fraud prevention. Analyze data with Python or Scala, Spark, AWS, and relational databases.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring and retraining models, and applying responsible AI practices. The role collaborates with Product and Data Science teams and uses Python, Java, Scala, or related languages.