Top Tech Jobs & Startup Jobs in San Francisco, CA

Lead the architecture, development, deployment, and optimization of large-scale AI systems, including foundation models, LLM inference, agents, vector search, guardrails, evaluation, governance, and observability. Define enterprise AI architecture and long-term platform strategy while improving cost, latency, throughput, safety, and reliability. Lead multi-year initiatives, establish responsible AI standards, collaborate across research and product teams, and mentor senior technical leaders.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, and model evaluation. Establish enterprise AI architecture standards, improve system cost, latency, throughput, and scalability, and promote responsible AI practices. Lead technical direction, cross-functional collaboration, mentoring, research-to-production efforts, and multi-cloud AI strategies.

Develop and deploy large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish governance standards, lead technical reviews, contribute to foundational AI roadmaps, and mentor senior AI engineering leaders.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead multi-model orchestration, champion responsible AI, and mentor engineering teams. Partner with research, product, and program teams to advance AI capabilities from experimentation through production.

Leads technical vision and architecture for Card Tech, delivering hands-on software, modernizing monoliths into microservices and event-driven platforms, and establishing scalable API and platform standards. Mentors engineers, drives engineering excellence, leads cross-divisional initiatives, evaluates non-functional system characteristics, and applies agentic AI coding tools to accelerate secure, high-quality development.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, and model governance. Define enterprise AI architecture and long-term platform strategy while improving performance, cost, scalability, latency, and reliability. Lead AI safety and transparency standards, drive organization-wide platform initiatives, collaborate with cross-functional teams, and mentor senior engineering and research leaders.

Design, develop, deploy, and optimize enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish AI architecture standards, improve model performance and efficiency, lead multi-cloud strategies, and mentor engineering teams while promoting responsible AI practices.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, lead technical reviews and AI governance, contribute to long-term platform strategy, and mentor senior AI engineering leaders.

Design, develop, deploy, optimize, and support foundational AI systems, including LLM inference, model training, agentic workflows, vector search, guardrails, and multi-model orchestration. Lead cost-performance governance, technical design reviews, AI standards, and roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference cost. Mentor senior technical leaders and collaborate with engineers, scientists, product managers, and program managers.

Design, develop, deploy, and operate machine learning models and components at scale. Build data pipelines, cloud-based ML architectures, production services, and automated testing and deployment workflows. Collaborate with Product and Data Science teams, retrain and monitor production models, optimize algorithms and infrastructure, and apply responsible AI, governance, security, and explainability practices.