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

Lead design, build, deploy, and support GenAI platform services (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, collaborate cross-functionally, contribute to technical vision and roadmap.

Lead design, development, deployment, and support of large-scale AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and optimization to improve scalability, cost, latency, and throughput while guiding technical vision and cross-functional delivery.

Lead research and development of emerging ML (embeddings, foundation models, transformers). Build and productionize machine learning models at scale, partner with product and engineering, analyze large-scale transactional and clickstream data, and run experiments to improve customer experiences across marketing, servicing, and fraud domains.

Lead design, build, and operate scalable GenAI platform components including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Collaborate cross-functionally, invent LLM optimization techniques, and shape long-term technical vision for foundational AI systems.

Lead model risk audits across development life cycles: review design, training, evaluation, validation, and implementation of ML models. Partner cross-functionally to surface model risk issues, apply NLP and Generative AI methods, and communicate findings to stakeholders and the Audit Committee.

Design, build, deploy, and support large-scale AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, and observability. Collaborate cross-functionally, optimize model performance and cost, and contribute to technical vision and roadmap for foundational AI platforms.

Design, build, deploy, and maintain production-scale ML models and pipelines. Collaborate with cross-functional Agile teams to implement cloud-based architectures, optimize model performance, automate CI/CD, and ensure model governance, monitoring, and responsible AI practices.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

The role involves designing, developing, and supporting AI software components, leveraging various AI technologies, and optimizing performance for large-scale AI systems.

The role involves architecting a scalable AI platform, building distributed compute solutions, optimizing technologies, and mentoring internal talent in AI and ML.