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

Lead AI Engineer responsible for developing AI-powered products, optimizing large language model inference, and collaborating across teams to enhance customer interactions and business value.

The role involves leading a team to develop machine learning models for Travel Intelligence, focusing on the Hotels domain. Responsibilities include collaborating with cross-functional teams, leveraging technologies for data analysis, and delivering products that enhance customer experiences.

The Senior Lead Machine Learning Engineer will design, implement, and scale ML applications, ensuring performance and high availability while collaborating with cross-functional teams.

Lead AI Engineer responsible for designing and developing AI-powered products, utilizing ML algorithms, and optimizing large-scale AI systems. Collaborate with cross-functional teams to enhance customer experience and drive AI initiatives at Capital One.

Design, train, validate, and implement machine learning models using large-scale bureau and customer data. Partner with engineers and product managers to build data products, extract insights from numeric and textual data, and translate technical results into business impact using cloud and open-source tools.

Lead design, build, deploy, and maintain production ML models and pipelines at scale. Collaborate with product and data science teams to architect cloud-based ML systems, automate CI/CD, monitor and retrain models, ensure model governance and explainability, and drive high availability and performance.

Lead design, development, deployment, and support of foundational AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability). Optimize large-scale AI performance (cost, latency, throughput), leverage OSS and SaaS stacks, mentor teams, and shape long-term platform vision.

Lead design, development, deployment, and support of foundational AI systems including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI performance (latency, throughput, cost), collaborate cross-functionally, and contribute to technical vision and roadmap for production AI infrastructure.

Lead enterprise-scale Generative AI programs from research to production, focusing on multi-agent conversational systems, agentic architectures, pretraining/finetuning, and inference optimization. Drive cross-functional delivery, manage technical risk, establish evaluation and deployment frameworks, partner with risk/compliance, and ensure scalable, secure adoption of AI solutions.

Lead development of next-generation entity resolution ML solutions using transformers, graph ML, and large-scale data pipelines. Partner with cross-functional teams to design, train, validate, and deploy models on cloud platforms, leverage agentic AI workflows, and translate technical work into business outcomes.