Top Generative AI Jobs in Los Angeles, CA

Conduct research in generative media, large generative models, GenAI efficiency, personalization, 3D/4D generation, visual-language models, large-scale data, and inference frameworks. Collaborate with researchers and engineers to develop state-of-the-art technology, with opportunities to publish papers and file patents. The 13–16 week internship requires working in an office at least four days per week.

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.

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.

Develop and optimize generative AI architectures for material and molecular discovery, including diffusion models, flow matching, and GFlowNets. Integrate reward signals from learned predictors and simulations, manage data ingestion and quality control, develop computational workflows, and deploy production-grade ML capabilities. Collaborate with ML researchers, computational scientists, engineering, product, and R&D teams. The role requires expertise in applied ML, scalable frameworks, cloud platforms, and material discovery, with preferred experience in active learning, Bayesian optimization, guided generation, and agentic or LLM methods.

Build and operate a secure, locally hosted LLM platform for enterprise engineering and business use. Responsibilities include model evaluation and optimization, inference serving, RAG and search pipelines, permission-aware retrieval, security controls, evaluation frameworks, observability, safe agent tooling, integrations, CI/CD, reliability, and lifecycle management. The role leads architecture and roadmap decisions while partnering cross-functionally to deliver measurable, production-ready generative AI capabilities.

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 architecture and technical direction of Generative AI solutions for Life and Annuities insurance operations. Design secure, scalable AI workflows using RAG, agents, vector search, and human oversight. Integrate AI with policy administration, underwriting, TPA, CRM, document, and partner platforms. Establish governance, security, privacy, monitoring, compliance, and nonfunctional standards while guiding cross-functional engineering teams and influencing enterprise technology strategy.

Researches, designs, develops, deploys, integrates, evaluates, and tests AI/ML and Generative AI solutions for mission-focused applications. Builds LLM-based systems using retrieval-augmented generation, agentic workflows, cloud services, and AI/ML frameworks. Participates in the full software engineering lifecycle, collaborates with distributed Agile teams, and communicates technical concepts to technical and non-technical stakeholders.

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.

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.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

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.

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.

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.