Top Generative AI Jobs in NYC, NY

Develop generative AI and machine learning solutions for customer-facing and internal applications. Responsibilities include partnering with data scientists, engineers, and product managers; analyzing large numeric and textual datasets; building, training, validating, evaluating, and implementing models; researching emerging AI technologies; and translating technical findings into business outcomes.

Graduate intern supporting generative AI application development for New York City social services. Responsibilities include evaluating model quality and safety, debugging and optimizing AI applications, preparing and validating data, designing AWS Bedrock and Anthropic Claude solutions, reviewing Python and ReactJS code, documenting technical artifacts, and tracking project tasks.

Lead end-to-end design, development, and delivery of enterprise Generative AI and ML solutions. Architect scalable LLM, RAG, and agent-based systems, drive technical decisions, implement production-ready MLOps/CI-CD, and mentor engineers. Collaborate with product and business stakeholders, ensure monitoring, security, and operational support, and evaluate emerging AI technologies to improve delivery.

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.

Design and deploy production-grade generative AI applications, LLM solutions, and intelligent agents that automate complex enterprise workflows. Build scalable Python systems, RAG data layers, and containerized deployments in OpenShift. Apply rigorous engineering practices, including testing, code reviews, documentation, and CI/CD. Research emerging AI techniques, mentor colleagues, deliver iteratively in an agile environment, and implement responsible AI guardrails to mitigate operational and regulatory risks.

Build secure, scalable generative AI services, applications, APIs, microservices, and agentic workflows. Own architecture from proposal through production, develop React and TypeScript interfaces, and collaborate with product, data science, and DevOps teams. Implement deployment, monitoring, logging, enterprise security, and reliability capabilities while mentoring engineers and delivering resilient production systems.

In this role, you will design and deploy advanced machine learning and Generative AI solutions, leading projects from conception to deployment while mentoring junior team members.

Develop generative machine learning technologies and products serving millions of users. Build state-of-the-art image, video, language, and audio generation pipelines; deploy generative ML experiences on devices; create augmented reality experiences using generative and diffusion models; and collaborate with cross-functional teams to prototype new products.

Develop machine learning products and generative pipelines for image, video, language, and audio generation. Build on-device generative ML and augmented reality experiences using diffusion models, collaborate with cross-functional teams to prototype products, and solve open-ended technical problems. The role requires strong Python or C++ development, deep learning framework expertise, and research or engineering experience in generative or efficient models.

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 deliver generative AI and computer vision models and on-device inference for consumer AR experiences. Prototype research advances, optimize models (compression, quantization, distillation), collaborate across teams, and productionize scalable inference pipelines for mobile and wearable platforms.

Build and deploy generative AI and LLM solutions for consulting clients across the full lifecycle, including architecture, prototyping, prompt engineering, RAG, fine-tuning, application development, MLOps, evaluation, and responsible AI. Collaborate with architects and cross-functional teams, document best practices, explain technical concepts, research emerging techniques, and distinguish GenAI use cases from traditional NLP applications.

Leads Slate’s GenAI strategy, architecture, and production deployment across vehicle engineering, manufacturing, supply chain, software, and go-to-market operations. Owns enterprise-scale GenAI platforms, agentic systems, data and context layers, model serving, evaluation, guardrails, and observability. Applies physical AI to robotics, computer vision, predictive maintenance, and factory feedback loops. Recruits and develops a high-performing technical team while remaining hands-on with coding, architecture, stakeholder engagement, and technical standards.

Creates commercial AI-generated still images and videos from brief through final delivery. Develops visual concepts, storytelling, shot design, and finished work using generative AI alongside compositing, retouching, color grading, VFX, and other post-production techniques. Collaborates with creative and production teams, incorporates client feedback, guides junior artists, works directly with clients when needed, and helps establish evolving AI workflows and best practices.

Lead research and development of generative AI and recommendation solutions for Netflix promotional assets and content discovery. Responsibilities include tracking LLM research, developing summarization and LLM-as-judge systems, training and evaluating models, optimizing generated content for audience impact, partnering with engineering teams, defining ML roadmaps, and communicating technical strategies to stakeholders.

Design, develop, deploy, and optimize Generative AI applications using LLMs and AI frameworks. Build scalable APIs, automate workflows, integrate foundation models with enterprise systems, and improve model performance through testing and experimentation. Implement monitoring, quality assurance, governance, security, compliance, documentation, and risk mitigation. Evaluate emerging technologies, support proofs of concept, and collaborate with product, engineering, and business teams.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering frameworks, supports AI strategy, ensures secure and responsible deployment, mentors engineers, evaluates emerging technologies, and translates business needs into scalable technical architectures.

Partners with customers and internal teams to design, deploy, and optimize generative AI and LLM inference solutions. Develops proofs of concept, supports NVIDIA technology adoption, analyzes AI workload performance and power efficiency on Kubernetes, and advises developers, researchers, and data scientists. The role requires expertise in deep learning frameworks, GPU orchestration, MLOps, containerization, observability, and LLM inference, with occasional travel to conferences and customers.

Deploys, integrates, and scales enterprise generative AI solutions within customer engineering teams. Builds intelligent agents and multi-agent systems using Vertex AI, ADK, and MCP; connects AI systems to enterprise databases; develops real-time data pipelines; and operationalizes LLMs across cloud environments. The role also includes model training, tuning, evaluation, deployment, vector-based grounding, cloud infrastructure integration, stakeholder communication, and translating business goals into technical architectures.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

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

Lead and execute Generative AI strategy for Global Markets Regulatory Reporting. Define engineering approach, design scalable GenAI solutions, set technology stack, oversee cross-team design and testing, integrate GenAI tools, and drive implementation for multi-tier, microservices-based platforms to deliver business outcomes.

Reviews and evaluates AI-generated text, translations, audio, images, video, and other data; identifies errors and quality issues; annotates and labels content; provides written feedback; follows detailed project guidelines; and completes independent assignments within deadlines.

Design, build, and deploy production-ready intelligent solutions using machine learning, deep learning, generative AI, and large language models. Develop RAG pipelines, integrate AI services, manage model deployment and MLOps workflows, and apply strong software engineering practices. Collaborate with engineering, product, and data teams in a distributed environment to create scalable, reliable, responsible AI-powered products.