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

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and deployment. Lead research initiatives spanning large-scale deep learning, language models, optimization, fine-tuning, and production inference. Collaborate with data scientists, software and ML engineers, product managers, and business leaders to translate research into scalable AI products and customer experiences.

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 AI architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term roadmaps, and mentor engineering associates.

Leads an AI engineering team developing scalable generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and agentic workflows. Sets technical vision, makes build-versus-buy decisions, optimizes production AI systems, operationalizes Responsible AI standards, develops team roadmaps, collaborates cross-functionally, and mentors engineering talent.

Conduct applied AI research and build foundation models, including vision-language models, across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Develop research methods, reproduce and extend published work, optimize training and inference, and contribute to technical reports and conference submissions. Own research problems and agendas while translating complex findings into business outcomes.

Leads data science and model risk initiatives, partnering with data scientists, engineers, and product managers. Builds, evaluates, validates, and implements machine learning models using large-scale data and technologies including Python, AWS, H2O, and Spark. Translates complex statistical findings into business outcomes, researches emerging methods, validates and backtests models, and develops talent. Requires quantitative education, substantial analytics experience, machine learning expertise, and relational database experience.

Develop machine learning and statistical models for Business Cards marketing, including design, training, evaluation, validation, and implementation. Analyze large numeric and textual datasets using Python, cloud computing, and big-data technologies. Partner with data scientists, engineers, product managers, analysts, and marketers to translate insights into personalized campaigns and measurable business outcomes.

Design, build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML infrastructure, data pipelines, cloud architectures, distributed systems, model monitoring, retraining, testing, CI/CD, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while applying advanced modeling techniques and managing large-scale containerized ML systems.

Build and deploy scalable, responsible AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to platform roadmaps, and mentor engineering staff.

Design, build, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor engineering teams.

Leads product strategy and operations for AI/ML platforms, enabling associates to build, train, deploy, and scale models and generative AI applications. Defines roadmaps, prioritization, operational frameworks, and strategic planning processes while advocating for data scientists and engineers. Manages teams, partners with Product, Engineering, TPM, and business functions, oversees concurrent programs, and applies analytics to product, marketing, and credit risk decisions.