Top Generative AI Jobs in San Francisco Bay Area, CA

Build secure, scalable generative AI services, applications, APIs, microservices, and agentic workflows for enterprise customers. Own system architecture from proposal through deployment, develop React and TypeScript interfaces, and collaborate with product, data science, and DevOps teams. Implement continuous deployment, monitoring, logging, security, and reliability practices while leveraging cloud platforms, containers, vector databases, and modern AI development tools. Mentor engineers and take ownership of resilient production code.

Designs, develops, fine-tunes, evaluates, and deploys generative AI and LLM solutions for clients. Responsibilities include prompt engineering, RAG architecture, model selection, application development, MLOps integration, cloud deployment, evaluation, security, compliance, and responsible AI. The consultant collaborates with technical and nontechnical stakeholders, researches emerging AI methods, documents practices, and helps distinguish GenAI use cases from traditional NLP applications.

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.

Own the production deployment of neural and generative models for Waabi’s multi-sensor simulator. Responsibilities include optimizing models with compilation, quantization, mixed precision, CUDA, and custom kernels; building numerical parity benchmarks; developing model export, versioning, registry, and build tooling; collaborating with research teams; and managing GPU workloads in cloud environments.

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.

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.

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.

Lead product strategy and execution for C3 Generative AI. Define technical requirements, collaborate with engineering and data science, drive roadmaps, manage go-to-market, monitor product metrics, and iterate based on customer feedback to deliver scalable enterprise AI solutions.

Conduct research to develop generative and sampling models for molecular systems, integrating probabilistic inference, deep learning, and reinforcement learning. Design, train, prototype, and benchmark diffusion, autoregressive, flow-based, and latent-variable architectures. Map between simulation and reality, collaborate with chemists and physicists, and work with engineers to deploy scalable research on distributed compute. Contribute end-to-end from ideation to production in a hybrid San Francisco/New York environment.

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.

Develop, train, evaluate, and deploy spatial generative AI models for home renovation and design. Define inference framing, prepare image-based datasets with labeling partners, iterate with user feedback, and deploy performant models into production architecture.

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.

Develop infrastructure and tools to integrate Generative AI into enterprise applications. Build scalable end-to-end ML pipelines on heterogeneous hardware, collaborate with data scientists to train/fine-tune models, lead product development for the Generative AI suite, mentor junior engineers, and own production deployments.

Lead design and build of Generative AI infrastructure and tools, deploy large-scale ML/LLM models to production, enable scalable ML pipelines on heterogeneous hardware, collaborate with product and data science, lead cross-team architecture and technical discussions, manage projects, and mentor engineers.

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.