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

Leads machine learning engineering teams while designing, deploying, scaling, monitoring, and governing production ML models and platforms. Responsibilities include building distributed ML systems, cloud architectures, data pipelines, CI/CD automation, model retraining, responsible AI practices, and resilient production services. The role combines hands-on technical leadership with mentoring, recruiting, talent development, and cross-functional collaboration with Product and Data Science teams.

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 system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to strategic roadmaps, and mentor senior technical staff.

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 performance governance, lead design reviews, define technical roadmaps, and mentor senior AI engineering talent.

Lead development of scalable AI and machine learning solutions for customer-facing applications. Build, fine-tune, evaluate, and validate NLP models and large language models, partnering with engineering, product, and design teams to operationalize them at scale. Research emerging generative AI technologies, deliver production-ready libraries and solutions, translate technical findings into business outcomes, and provide technical leadership across cross-functional teams.

Leads data science for AI Foundations, developing and deploying scalable NLP, machine learning, and generative AI solutions. Partners with data scientists, engineers, and product managers to train, evaluate, validate, and operationalize models serving millions of customers. Applies expertise in large language models, optimization, self-supervised learning, explainability, or RLHF while mentoring talent and translating technical work into business outcomes.

Conduct applied AI research focused on recommendation systems and foundation models. Design, train, evaluate, validate, and implement large-scale models using PyTorch, AWS, Hugging Face, and Lightning. Collaborate with data scientists, engineers, and product managers to deliver AI products, translate research into business outcomes, and publish technical findings. Own research agendas, reproduce and extend peer-reviewed work, and optimize models for large-scale training and inference.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, orchestration, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Establish AI governance standards, lead technical reviews, shape long-term platform strategy, and mentor senior AI engineers. Collaborate with engineering, research, product, and program teams to deliver responsible AI products.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, orchestration, cost-performance governance, technical reviews, roadmap development, and responsible AI standards. Mentor senior technical staff and apply advanced optimization techniques to improve model scalability, latency, throughput, hardware utilization, and inference cost.

Build, deploy, optimize, and monitor production machine learning models and infrastructure. Develop ML applications, data pipelines, cloud architectures, and distributed systems using technologies such as PyTorch, TensorFlow, Spark, Ray, and Kubernetes. Collaborate with Product and Data Science teams, automate testing and deployment, maintain model governance, and apply responsible and explainable AI practices.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model 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 technical reviews, and mentor senior engineering leaders while applying emerging research responsibly in production.