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

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability SLOs, and technical roadmaps while collaborating across engineering, research, infrastructure, and product teams. Lead deployment reviews for security and compliance and mentor senior technical staff.

Build, deploy, scale, and monitor production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing algorithms and data pipelines, operating distributed systems and cloud services, automating testing and deployment, maintaining models, and applying responsible AI, governance, security, and explainability practices. The role collaborates with Product, Data Science, and Agile engineering teams.

Leads an AI engineering team building scalable, responsible generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and governance. Establishes technical strategy, optimizes production AI systems for cost and performance, manages cloud deployments, translates enterprise goals into roadmaps, and develops engineering talent.

Conduct applied AI research focused on multimodal guardrails and vision-language models. Build and evaluate foundation models across training, validation, optimization, and production implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Develop research libraries and platform code, investigate emerging methods, document findings, and contribute to technical reports or conference publications.

Leads an enterprise data science team developing agentic AI products and machine learning solutions. Responsibilities include defining modeling strategy, designing experiments, analyzing telemetry, influencing senior leaders, validating and stress-testing models, and overseeing the full model lifecycle from design through implementation. The role partners with engineering and research teams and requires extensive experience in large-scale analytics, machine learning, relational databases, cloud computing, and open-source programming.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, reliability, and hardware utilization. Define system architectures and SLOs, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Designs, develops, deploys, and optimizes production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. The role establishes AI cost-performance standards, leads technical design reviews, contributes to platform roadmaps, optimizes model and hardware utilization, and mentors senior AI engineers while delivering scalable and responsible AI capabilities.

Lead the engineering organization responsible for large-scale generative AI training infrastructure, including distributed training, reinforcement learning, GPU scheduling, fault tolerance, model experimentation, and cost governance. Define AI platform strategy, responsible AI standards, observability, governance, and long-term roadmaps. Partner with research, product, compliance, and risk teams while hiring, mentoring, and developing technical talent across multiple teams.

Leads personal loan acquisition data science, developing and deploying machine learning models for customer targeting, campaign optimization, underwriting, pricing, and verification. Partners with data scientists, engineers, and product managers; analyzes large-scale numeric and textual data using Python, Spark, AWS, and related technologies; validates and backtests models; translates technical findings into business outcomes; and develops team talent.

Designs, develops, deploys, and supports production AI systems, including foundation models, LLM inference, agentic workflows, guardrails, evaluation, and observability. Owns complex AI architecture, reliability objectives, governance, and optimization for cost, latency, throughput, and hardware utilization. Partners across engineering, research, product, and program teams; leads technical reviews; contributes to long-term platform strategy; and mentors senior technical associates.