Top Tech Jobs & Startup Jobs in San Francisco, CA

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, latency, throughput, cost, and hardware utilization across production platforms. Own complex AI architecture, reliability objectives, security, and compliance reviews while contributing to technical roadmaps and mentoring engineering colleagues.

Design, build, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Define reliability objectives, lead architecture and technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering associates.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models throughout the development lifecycle, optimize training and inference, and translate research into scalable production systems. Collaborate with data scientists, software and ML engineers, and product managers to deliver AI-powered customer experiences. Lead research initiatives, establish research agendas, publish high-quality work, and mentor junior researchers or engineers.

Build and deploy foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize large-scale AI systems for cost, latency, throughput, and hardware utilization. Lead architecture and governance reviews, define long-term technical roadmaps, integrate heterogeneous AI components, enforce responsible AI standards, and mentor senior technical staff.

Conduct applied research in AI foundations, large language models, and agentic AI. Design, train, evaluate, validate, and deploy foundation models at scale using modern open-source and cloud technologies. Collaborate with data scientists, engineers, and product managers to deliver AI-powered products, develop platform and solution code, evaluate emerging methods, and communicate findings through internal reports and conference publications.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI models and infrastructure for cost, latency, throughput, and hardware utilization. Architect multi-model orchestration pipelines, define technical roadmaps and engineering standards, lead design reviews, and mentor senior AI engineers.

Designs, develops, deploys, and optimizes foundational AI systems, including LLMs, agents, vector search, guardrails, evaluation, and multi-model orchestration. The role improves model performance, scalability, latency, throughput, hardware utilization, and cost efficiency. It establishes AI governance and engineering standards, leads technical reviews, contributes to long-term platform strategy, and mentors senior AI engineers while applying state-of-the-art research techniques in production.

Design, develop, deploy, and support production AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Define reliability objectives, own complex architectures, ensure security and compliance, contribute to technical roadmaps, and mentor engineering staff.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for scalability, latency, throughput, hardware utilization, and cost. Lead orchestration architecture, governance reviews, design councils, technical roadmaps, and mentorship while integrating heterogeneous AI systems into responsible production pipelines.

Analyze complex data, develop business intelligence tools, dashboards, and metrics, and build data solutions for self-service analytics. Manage data quality, metadata, lineage, governance, security, and access controls. Partner with business and technology teams to deliver insights and improve data management. The role uses Python, R, Spark, SQL, Snowflake, AWS, and visualization tools.