Top AI Jobs in San Francisco Bay Area, CA

Build and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, guardrails, and orchestration pipelines. Lead cost-performance governance, improve model scalability and hardware utilization, shape technical roadmaps, establish AI engineering standards, and mentor senior technical staff. The role requires applying current AI research to reliable, responsible production systems across cloud and large-scale infrastructure.

Build and ship full-stack AI products for FDA regulatory workflows. Responsibilities include architecting scalable systems, ingesting and structuring heterogeneous regulatory data, maintaining data quality and governance, developing RAG and agent workflows, creating evaluation benchmarks, collecting human feedback, and collaborating with FDA experts. The role involves frontend and backend development, debugging, performance optimization, and owning features from concept through delivery.

Owns technical relationships with enterprise customers, designing and implementing AI solutions on the Moveworks Platform. Responsibilities include architecture, API integrations, workflow automation, customer advisory, implementation leadership, product feedback, and reusable solution development. The role requires frequent customer engagement, executive communication, collaboration with engineering and product teams, and travel up to 25%.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, model evaluation, and orchestration pipelines. Lead cost-performance governance, technical design reviews, AI standards, roadmap development, and mentoring. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while ensuring responsible AI deployment.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, AI standards, and design reviews while mentoring senior technical staff. Partner with research, engineering, product, and program teams to establish foundational AI roadmaps and deliver scalable, responsible systems.

Designs, develops, deploys, and supports large-scale AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes AI infrastructure for scalability, latency, throughput, cost, and hardware utilization; defines reliability objectives; leads architecture and technical reviews; and mentors engineering staff.

Conduct applied research and build AI foundation models, including large language and agentic AI systems, through design, training, evaluation, validation, and deployment. Partner with data scientists, engineers, and product managers to translate research into scalable customer-facing products. Lead research initiatives, investigate state-of-the-art methods, optimize large-model training and inference, and guide junior researchers or engineers.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Optimizes large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads architecture and design reviews, establishes AI engineering standards, drives technical roadmaps, and mentors senior AI engineers.

Design, develop, deploy, and support foundational AI systems, including LLM training and inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define AI service-level objectives, lead technical reviews, ensure security and compliance, contribute to long-term roadmaps, and mentor engineering staff.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and optimize foundation models through design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products, translate research into business outcomes, and publish technical findings. Requires advanced research expertise in deep learning, LLMs, optimization, fine-tuning, data preparation, and reproducible experimentation.

Design, develop, deploy, and support production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, lead AI governance and technical design reviews, establish engineering standards, and mentor senior technical staff.

Leads an AI engineering team building scalable, responsible generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and governance. Establishes technical strategy, optimizes production AI systems for cost and performance, manages cloud deployments, translates enterprise goals into roadmaps, and develops engineering talent.

Design, develop, deploy, and optimize foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead multi-model orchestration, cost-performance governance, technical standards, roadmap development, and design reviews. Mentor senior AI engineers while applying advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs.

Conduct applied research in AI foundations, large language models, and agentic AI. Design, train, evaluate, validate, and deploy foundation models at scale using modern deep learning frameworks and cloud infrastructure. Develop novel methods, deliver platform or product code, collaborate with data scientists, engineers, and product managers, and communicate findings through technical reports or conference publications.

Build and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Design multi-model orchestration pipelines, improve model cost, latency, throughput, and scalability, establish AI governance standards, lead technical reviews, and mentor senior AI engineering staff. Collaborate with engineers, scientists, technical program managers, and product managers to deliver responsible AI capabilities.

Build and optimize foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead multi-model orchestration, cost-performance optimization, technical standards, and design reviews for scalable production AI. Partner cross-functionally, contribute to long-term AI roadmaps, and mentor senior technical leaders while applying state-of-the-art research to responsible AI systems.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance governance for scalable AI platforms, establish engineering standards, contribute to technical roadmaps, and mentor senior AI engineers. Collaborate with scientists, engineers, technical program managers, and product managers to deliver responsible AI products.

Design, develop, deploy, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI platforms for cost, latency, throughput, scalability, and hardware utilization. Lead architecture and governance reviews, shape long-term platform roadmaps, establish engineering standards, and mentor senior AI engineers across the organization.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models, optimize training and inference, develop scalable AI solutions, and translate research into production systems. Lead cross-functional research initiatives with data scientists, software engineers, ML engineers, and product managers while pursuing impactful research and mentoring junior team members.

Build and optimize foundational AI systems, including LLM training and inference, agentic workflows, vector search, guardrails, evaluation, and observability. Partner with engineering, research, product, and program teams to deliver scalable AI products. Lead model and infrastructure optimization for cost, latency, throughput, and hardware utilization; establish governance and engineering standards; contribute to technical roadmaps; and mentor senior AI leaders.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization across production systems. Own complex AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews, collaborate across engineering and product teams, and mentor senior and principal associates.

Design, develop, deploy, optimize, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance optimization across scalable AI platforms, establish engineering standards, contribute to long-term technical roadmaps, and mentor senior AI engineers.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to platform roadmaps, and mentor engineering teams.

Leads and scales AI engineering teams responsible for production LLM systems, agents, integrations, and shared AI platforms. Oversees architecture, delivery, reliability, cost, latency, evaluation, observability, governance, security, and operational practices. Partners with product and business leaders to prioritize investments, manage dependencies, and establish reusable engineering standards. Develops engineering leaders and drives alignment across teams while ensuring safe, scalable AI adoption.