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

Build and deploy machine learning and data science models across the credit card lifecycle, including marketing, acquisitions, underwriting, and fraud prevention. Partner with data scientists, software engineers, and product managers; analyze large numeric and textual datasets; develop models through design, training, validation, evaluation, and implementation; and translate complex analytical findings into business outcomes.

Conduct applied AI research focused on foundation models, evaluation, and agentic workflows. Build, train, validate, and deploy large-scale deep learning models using technologies such as PyTorch, AWS, Hugging Face, and Lightning. Lead research initiatives, develop platform and solution-level code, scale models for production, collaborate with engineering and product teams, and translate research findings into customer-facing AI products.

Lead enterprise AI engineering strategy and teams building scalable agentic AI platforms. Oversee foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and AI operations. Drive build-versus-buy decisions, optimization, responsible AI practices, multi-year technical roadmaps, and cross-functional alignment while developing senior engineering talent and supporting hundreds of production use cases.

Design, develop, deploy, and support foundational AI systems, including large language model inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, scalability, and hardware utilization. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor senior technical staff.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, and observability. Lead multi-model orchestration, cost-performance governance, architecture reviews, technical roadmaps, and responsible AI standards. Mentor senior technical staff and collaborate with engineers, scientists, product managers, and program managers to deliver scalable AI products.

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

Develop machine learning models for business card credit underwriting across the full lifecycle, from design and training through validation and implementation. Collaborate with data scientists, engineers, product managers, and business stakeholders to develop scalable solutions using large datasets and cloud technologies. Translate complex analysis into business outcomes and apply statistical methods including classification, clustering, sentiment analysis, time series, and deep learning.

Build and scale enterprise machine learning platforms and production ML systems. Develop models, data pipelines, distributed training workflows, Kubernetes operators, SDKs, and developer tooling using Python and cloud technologies. Deploy, monitor, retrain, and govern ML models while applying CI/CD, testing, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile engineering teams, and contribute to open-source Kubeflow.

Design, develop, deploy, and support foundational AI systems, including LLM inference, multimodal safety guardrails, agents, similarity search, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Lead architecture and technical reviews, define AI reliability objectives, ensure security and compliance, and mentor engineering staff.

Leads applied AI research and development for foundation models and large-scale machine learning systems. Partners with data scientists, engineers, product managers, and academic researchers to define research directions, train and evaluate models, optimize inference and training, and deploy AI capabilities into products. Mentors applied scientists and managers, represents the organization externally, and translates advanced research into scalable customer experiences.