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

Leads the architecture, development, deployment, and optimization of large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establishes enterprise AI architecture, performance and safety standards, and multi-year platform strategies. Partners across engineering, research, product, and program management while mentoring technical leaders and driving responsible AI adoption.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability objectives, governance, security, and compliance while collaborating across engineering, research, product, and program teams. Lead technical reviews, shape foundational AI roadmaps, and mentor senior technical staff.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead hybrid and multi-model orchestration, promote responsible AI, and mentor engineering teams while influencing technical strategy and research-to-production processes.

Build, deploy, scale, and operate production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, managing cloud-based distributed systems, automating testing and deployment, monitoring production models, and applying responsible AI practices. The role collaborates with Product and Data Science teams in an Agile environment and uses technologies such as Python, PyTorch, Spark, Kubernetes, and cloud platforms.

Design, develop, optimize, deploy, and support foundational AI systems, including LLMs, agents, vector search, model evaluation, orchestration pipelines, and responsible AI controls. Lead cost-performance governance, technical design reviews, roadmap development, and optimization of model and hardware utilization. Mentor senior AI engineers while partnering with research, product, program, and engineering teams to deliver scalable production AI platforms.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, guide hybrid and multi-cloud strategies, and promote responsible AI practices. Lead technical direction, mentor engineering teams, collaborate with cross-functional stakeholders, and advance research techniques into production.

Design, build, deploy, monitor, and maintain production machine learning models and infrastructure. Develop optimized data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Apply modeling, feature selection, validation, responsible AI, and explainability practices while collaborating with product, data science, and engineering teams in an Agile environment.

Conduct applied research on large language models, training optimization, and fine-tuning. Design, train, evaluate, validate, and deploy AI foundation models using PyTorch, AWS, Hugging Face, and Lightning. Lead research initiatives across data science, engineering, and product teams, translating advances into scalable production systems. Candidates should have advanced academic training and experience with large-scale deep learning, model optimization, self-supervised learning, robustness, explainability, or RLHF, plus a record of impactful research and publications.

Lead data science and NLP initiatives for AI-powered products, partnering with engineers, machine learning specialists, and product managers. Build, fine-tune, evaluate, validate, and operationalize large language and machine learning models at scale. Apply advanced NLP, deep learning, and model training techniques while supporting model risk compliance. Translate technical findings into business outcomes and develop team talent.

Lead data science for US Card customer management by partnering with data scientists, engineers, product managers, and business teams. Build, validate, and implement machine learning models using large-scale numeric and textual data. Apply statistical modeling, clustering, classification, sentiment analysis, time series, and deep learning to generate customer insights and forecasts. Translate technical findings into business outcomes while contributing to innovation, stakeholder collaboration, and talent development.