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

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and production implementation. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Research LLMs, optimization, fine-tuning, and agentic AI; publish findings through technical reports and conference submissions; and independently pursue impactful research agendas.

Conduct applied AI research and build foundation models from design through training, evaluation, validation, and production implementation. Collaborate with data scientists, engineers, and product managers to deliver AI-powered products at scale. Research emerging methods, optimize large deep learning models, translate technical findings into business outcomes, and contribute to technical reports and conference publications. The role requires advanced expertise in foundation models, deep learning, optimization, NLP, or fine-tuning.

Leads applied AI research and teams developing foundation models and production-scale machine learning systems. Sets research strategy, mentors applied scientists, partners with engineering and product leaders, and translates advanced AI research into customer-facing products. Requires expertise in large-scale deep learning, LLMs, optimization, model deployment, research publications, and organizational leadership.

Leads data science efforts for foundational AI and automation across a global payments network. Partners with data scientists, engineers, and product managers to develop scalable machine learning engines, AI agents, merchant intelligence, and risk solutions. Serves as an NLP and LLM domain expert, fine-tuning and deploying models using PyTorch, AWS, vector databases, and modern AI technologies. Translates complex ML work into business value while mentoring teams and delivering production-scale platforms and solutions.

Build and operate foundational AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Lead architecture, optimization, reliability, and deployment of scalable AI platforms; improve GPU/TPU utilization, latency, throughput, and cost. Partner across engineering, research, product, and infrastructure teams, guide technical strategy, conduct deployment reviews, and mentor senior technical staff.

Build and deploy scalable generative AI platform services, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor senior technical staff.

Lead a product team building ML and GenAI infrastructure and model delivery for marketing and acquisitions. Define problem statements, set 3-year roadmaps tied to business outcomes, partner with data scientists and engineers, drive reusable scalable capabilities, influence cross-functional stakeholders, recruit and mentor talent, and measure success with data-driven metrics.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead orchestration, cost-performance governance, technical standards, design reviews, and roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost efficiency while mentoring senior technical staff and ensuring responsible AI practices.

Leads enterprise AI engineering strategy and multi-team delivery of scalable, responsible AI systems. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and production optimization. Establishes responsible AI standards, makes technology decisions, develops long-term platform roadmaps, partners with research and risk teams, and attracts and mentors engineering talent.

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