Top Generative AI Jobs in Los Angeles, CA

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.

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.

Own generative AI discovery, scaling, and maintenance for a portfolio of AI characters. Select and standardize models, build pipelines for face consistency and video, train and debug LoRAs, control GPU costs, design prompt architectures, define evaluation metrics and quality gates, and translate creative briefs into technical specs while testing frontier models and tooling to scale the studio.

Design, build, and deploy LLM-based applications and multi-step AI workflows (retrieval, orchestration, agents) using large-scale structured and unstructured data. Partner with engineering to productionize solutions, implement evaluation and safety measures, ensure compliance and data governance, and communicate results to technical and non-technical stakeholders.

Design, develop, and deploy production-grade generative AI and agentic systems (LLMs, RAG, AI agents). Build document intelligence, retrieval pipelines, vector search, and monitoring frameworks. Collaborate with risk, compliance, product, and engineering to deliver scalable, governed GenAI solutions in regulated environments.

Lead generative AI image production for realistic personas and models. Build and optimize scalable generative pipelines, automate batch workflows, evaluate new models/tools, define technical direction and best practices, and improve image quality, consistency, speed, and cost-efficiency.

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.

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

Review, evaluate, and annotate AI-generated text, images, audio, and video; perform quality checks for accuracy and guideline compliance; identify edge cases and inconsistencies; contribute to dataset creation and calibration; provide feedback to improve annotation workflows and guidelines.

Architect and optimize large-scale GPU AI infrastructure: profile distributed training/inference, diagnose networking and system bottlenecks, design high-performance clusters, lead POCs, automate benchmarking, and drive technical engagements with customers and internal teams.

Lead research and development of generative AI and NLP models for large-scale email data. Innovate on fine-tuning (LoRA/adapters), quantization-aware training, and knowledge distillation to meet strict latency budgets. Build scalable training/evaluation pipelines, establish robust evaluation frameworks (LLM-as-a-judge, synthetic and human-in-loop), integrate AI-augmented developer tools, and set long-term technical direction while mentoring researchers and driving production-ready model deployments.

The Senior Software Engineer will design and implement Generative AI solutions, collaborate with teams, enhance AI applications, and mentor others in best practices.

Design and execute ML research and end-to-end model development for generative and summary-generation systems. Lead rigorous experiments, translate findings for stakeholders, partner with engineers to deploy models, track state-of-the-art advances, and define roadmaps to improve promotional writing and discovery for members.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

Senior technical leader responsible for architecting and operationalizing generative AI across the enterprise. Drive end-to-end GenAI architecture, select models and agent frameworks, productionize RAG and agent solutions, ensure observability, guardrails, and cost optimization, integrate with AWS and DevOps toolchains, advise CXO stakeholders, shape AI strategy, and define governance and upskilling programs.

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.

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.