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

Develop and deploy production machine learning models and infrastructure at scale. Responsibilities include model development, data pipeline construction, cloud and Kubernetes operations, distributed data processing, CI/CD automation, model monitoring and retraining, responsible AI governance, and collaboration with product and data science teams. The role requires strong programming, machine learning, distributed systems, and production deployment expertise.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, shape long-term AI roadmaps, and mentor engineering staff.

Leads alternative data strategy for credit decisioning by evaluating vendors and datasets, engineering features, building and validating machine learning models, and developing scalable processing and monitoring systems. Partners with data scientists, software engineers, and product managers while translating complex analyses into business outcomes. The role requires expertise in large-scale data analysis, statistical modeling, machine learning, and relational databases, with hybrid office attendance.

Design, develop, deploy, and maintain production machine learning models and infrastructure. Build optimized data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Monitor and retrain models, ensure responsible and explainable AI practices, and collaborate with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Design, develop, optimize, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, orchestration, governance, and observability. Lead cost-performance optimization across production AI platforms, establish engineering standards, contribute to technical roadmaps, lead design reviews, and mentor senior AI engineers. Collaborate with research, engineering, program management, and product teams to deliver scalable, responsible AI products.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research advanced methods including deep learning, graph models, sequential models, optimization, robustness, explainability, and RLHF. Develop production-scale model libraries and solutions, publish findings, reproduce research, and pursue an independent research agenda.

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

Lead a data science team developing scalable AI and machine learning products for Capital One’s mobile app. Build, fine-tune, evaluate, and operationalize NLP and large language models using technologies such as PyTorch, AWS, Hugging Face, and LangChain. Partner with engineering, product, and design teams to deliver customer-facing AI features, translate technical findings into business outcomes, and develop talent.

Leads a marketing data science team developing and deploying machine learning models for customer acquisition and personalization. Responsibilities include designing, training, validating, evaluating, and implementing models; analyzing large-scale structured and textual data; developing Python libraries; collaborating with engineers, product managers, campaign managers, and analysts; and translating technical insights into business outcomes. The role also includes team leadership and talent development in a hybrid work environment.