Top AI & Machine Learning Jobs in San Francisco, CA

Designs, develops, deploys, and supports production AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimizes large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Owns complex architecture, defines reliability objectives, leads technical reviews, partners cross-functionally, and mentors engineering colleagues.

Build, deploy, optimize, and monitor production machine learning models and infrastructure. Responsibilities include developing ML applications, constructing data pipelines, operating distributed systems and cloud platforms, automating testing and deployment, retraining models, and applying responsible AI practices. Collaborate with Product, Data Science, and Agile teams to deliver scalable solutions using modern ML frameworks, containerized systems, and cloud architectures.

Design, build, deploy, and maintain production machine learning models and infrastructure. Develop optimized data pipelines, distributed ML systems, cloud-based architectures, and automated testing and deployment workflows. Monitor and retrain models, collaborate with product and data science teams, and apply responsible AI, model governance, security, and explainability practices. The role also involves optimizing algorithms and infrastructure, scaling resilient services, and supporting production operations.

Designs, develops, deploys, and optimizes production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. The role leads multi-model orchestration, cost-performance optimization, technical standards, and AI roadmap development while mentoring senior engineering talent and applying responsible AI practices.

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

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, and orchestration pipelines. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Establish governance and engineering standards, contribute to technical roadmaps, lead design reviews, and mentor senior AI engineering talent.

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 model performance, cost, latency, throughput, and hardware utilization; defines reliability objectives; owns complex AI architecture; leads technical reviews; and mentors engineering staff.

Design, develop, deploy, and support production 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, contribute to platform roadmaps, lead design reviews, and mentor senior AI engineering staff.

Design, develop, deploy, and optimize production AI systems involving foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead model orchestration and cost-performance optimization across hardware and cloud infrastructure. Establish engineering standards, oversee design reviews, contribute to AI platform roadmaps, and mentor senior technical staff while applying state-of-the-art research to scalable, responsible AI products.

Leads applied AI research teams and sets the strategic research roadmap. Partners with scientists, engineers, and product leaders to develop, train, evaluate, and deploy foundation models and other large-scale AI systems. Guides research into production-grade customer experiences, mentors applied scientists, represents the company within the research community, and translates advanced AI concepts into business impact. The role emphasizes large deep learning models, LLMs, optimization, production-scale inference, research publications, and people leadership.