Top Generative AI Jobs in NYC, NY

As a Senior Manager, Data Scientist, you will lead a team to develop generative AI solutions and build machine learning models, enhancing customer experiences and operational efficiency.

Design and implement generative ML systems (image, video, audio, multimodal LLMs) and deliver on-device and server-side inference. Build GenAI pipelines and AR experiences, prototype with cross-functional teams, and optimize efficient models for real-time mobile and wearable applications.

As a Principal Associate Data Scientist at Capital One, you will develop generative AI-based solutions, collaborate with cross-functional teams, and build machine learning models to derive insights from large data sets.

The role involves developing generative AI tools for financial modeling and transaction analysis, leveraging investment banking expertise without client-facing responsibilities.

As a Director, Data Scientist - Generative AI Systems, you will lead a team to develop AI products using NLP and generative AI technologies, partnering with cross-functional teams. Your role involves leveraging open-source programming and machine learning to convert customer data into actionable insights and enhance customer interactions with financial services.

As a Contract Equity Researcher, you will utilize equity research expertise to design and improve generative AI tools for financial analysis, producing high-quality outputs and collaborating with teams at Kensho to enhance model performance and accuracy.

The Senior Staff Generative AI Engineer role focuses on architecting and building generative AI applications and systems, leading automation efforts, and fostering engineering excellence while mentoring junior engineers.

Lead development and delivery of generative AI services across business units. Build AI-as-a-service platforms, guide model development (LLMs, RAG, agents), manage cross-functional teams and vendor relationships, implement governance and best practices, define engagement and funding models, and measure business impact of AI initiatives.

Lead end-to-end delivery of generative AI initiatives: scope, roadmap, prototyping, deployment, and adoption. Coordinate AI builders, product teams, and stakeholders, remove blockers, run iterative delivery cadences, and ensure measurable business impact.

Design, build, and optimize generative AI, RAG, and agentic RAG systems for drafting, search, and reasoning. Improve LLM outputs via prompt design, retrieval optimization, embeddings, reranking, and evaluation. Develop production-grade ML components and retrieval pipelines using OpenSearch/Solr, write maintainable Python code, and collaborate with engineering, product, and domain teams to improve relevance, accuracy, and user trust.

As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

The AI Analyst will research and implement AI solutions to enhance products and workflows, collaborating with cross-functional teams and supporting AI integration across the organization.

Lead architecture and delivery of Generative AI solutions across Life & Annuities insurance, defining secure, scalable patterns (RAG, vectors, agents), integrating AI with policy administration, underwriting, and third-party systems, and enforcing governance, observability, and compliance while mentoring engineering teams and guiding cloud and data platform choices.

The AI Engineer role involves developing generative AI agents, LLM services, and APIs, focusing on intent detection and context engineering in a fast-paced environment.

The Senior Data Scientist, Generative AI will develop and deploy ML models, improve data quality frameworks, and collaborate across teams to enhance AI-driven products for music and TV.

The Principal Engineer for Generative and Agentic AI will lead the design and development of resilient AI-powered systems for transforming customer interactions and will focus on integrating cutting-edge AI technologies into production-grade applications within the fintech sector.

Lead the design of generative AI solutions and multi-cloud architectures, translating business use-cases into scalable systems for enterprise deployments.

As a Software Engineer focused on generative AI, you will design scalable AI services, optimize APIs, and collaborate with cross-functional teams to enhance enterprise-wide transformation.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

Develop and deploy AI-powered chatbot applications, design conversational flows, integrate with various platforms, and optimize performance.

Design and build AI-powered chatbot solutions, optimize performance, and integrate with various platforms using NLP techniques.

Review text prompts and AI-generated videos to identify mismatches, visual or behavioral errors, and tag incorrect prompt text. Add concise annotations, perform self-QA, participate in calibration, document recurring model failures, and meet throughput and quality targets using structured annotation platforms.

Review text prompts and AI-generated video clips to identify mismatches, visual or behavioral errors, and tag incorrect prompt text. Create concise annotations, perform self-QA, follow project guidelines, attend calibrations, and document recurring issues while meeting throughput and quality targets.

Perform detailed annotation, labeling, and quality assurance on text, image, video, and multimodal datasets for generative AI. Evaluate prompts/responses, refine guidelines, identify edge cases, support data workflows, and collaborate to improve dataset and model quality.

Review, evaluate, and annotate AI-generated text, image, audio, and video in Simplified Chinese. Perform quality checks, identify edge cases, participate in calibration, and provide feedback to improve annotation guidelines and datasets for generative AI models.