Top Tech Jobs & Startup Jobs in San Francisco, CA

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.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models across design, training, validation, and deployment. Lead research initiatives, develop scalable model and platform solutions, collaborate with data science, engineering, and product teams, and translate research findings into production AI products. Required expertise includes deep learning, model optimization, research agenda ownership, and delivering high-volume inference systems.

Conduct applied AI research and develop foundation models across design, training, evaluation, validation, and production implementation. Collaborate with data scientists, engineers, machine learning specialists, and product managers to deliver scalable AI products. Research emerging methods, optimize large deep learning systems, publish findings, prepare technical reports, and independently pursue impactful research agendas.

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.

Conduct applied research on AI foundation models, large language models, and agentic AI. Design, train, evaluate, validate, optimize, and deploy large-scale models using modern open-source frameworks and cloud infrastructure. Collaborate with data scientists, engineers, product managers, and senior researchers to deliver AI-powered products, translate research into business outcomes, publish findings, and develop platform or solution-level code. Research areas include pretraining, fine-tuning, optimization, robustness, explainability, and reinforcement learning from human feedback.

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 involving foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, latency, throughput, hardware utilization, and cost. Lead architecture and governance reviews, define technical roadmaps and standards, integrate heterogeneous AI pipelines, and mentor senior engineering talent.

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.