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

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, and multi-model orchestration. Optimizes AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads governance reviews and design councils, contributes to technical roadmaps, establishes responsible AI standards, and mentors senior engineering leaders.

Design, develop, deploy, and support large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Establish governance and engineering standards, lead design reviews, contribute to platform strategy, and mentor senior AI engineers.

Build, deploy, scale, and monitor production machine learning models and infrastructure. Develop optimized data pipelines, cloud-based architectures, automated testing, and CI/CD workflows. Collaborate with product, data science, and Agile teams while applying responsible AI, model governance, and software engineering best practices. The role requires expertise in machine learning frameworks, distributed systems, cloud operations, Kubernetes, and production model deployment.

Develop and operate large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, guardrails, model evaluation, vector search, memory, governance, and observability. Lead architecture and optimization for cost, latency, throughput, reliability, and hardware utilization. Define AI service objectives, ensure security and compliance, guide technical roadmaps, and mentor engineering teams.

Build and implement machine learning models for personal loan acquisition, including customer selection, campaign optimization, underwriting, pricing, and verification. Partner with data scientists, engineers, and product managers; analyze large-scale numeric and textual datasets using Python, Spark, AWS, and related tools; validate and backtest models; and translate statistical insights into customer-focused business outcomes.

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Lead technical reviews and governance, define AI platform roadmaps, integrate heterogeneous AI systems, and mentor senior engineering talent.

Lead model validation and risk quantification across business domains. Partner with data scientists, engineers, and PMs to build and challenge production models using Python, Spark, H2O, and cloud tools. Drive model governance, backtesting, and present identified model risks to executives while mentoring teams and advancing validation frameworks.

Leads applied AI research and strategic direction for foundation models and large-scale machine learning systems. Responsibilities include designing, training, evaluating, validating, and deploying models; advancing research in areas such as LLMs, optimization, self-supervised learning, robustness, and explainability; guiding applied scientists and managers; collaborating with engineering, product, and academic partners; and translating research into scalable customer-facing products.

Conduct applied AI research focused on foundation models, LLM customization, and fine-tuning. Design, train, evaluate, validate, and deploy large-scale models using PyTorch, AWS, Hugging Face, and Lightning. Develop scalable research and platform code, reproduce and extend peer-reviewed work, design controlled experiments, publish findings, and collaborate with data scientists, engineers, and product managers to deliver AI-powered products.

Leads data scientists, business analysts, and data analysts in developing machine learning models from design through implementation. Partners with software engineering and product teams to deliver customer-focused products, using Python, cloud computing, Spark, H2O, and other technologies to analyze large-scale numeric and textual data. Translates complex technical findings into business goals while developing team talent and evaluating emerging methods.