Top AI & Machine Learning Jobs in San Francisco, CA

Design, build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML infrastructure, data pipelines, cloud architectures, distributed systems, model monitoring, retraining, testing, CI/CD, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while applying advanced modeling techniques and managing large-scale containerized ML systems.

Build and deploy scalable, responsible AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to platform roadmaps, and mentor engineering staff.

Design, build, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor engineering teams.

Leads product strategy and operations for AI/ML platforms, enabling associates to build, train, deploy, and scale models and generative AI applications. Defines roadmaps, prioritization, operational frameworks, and strategic planning processes while advocating for data scientists and engineers. Manages teams, partners with Product, Engineering, TPM, and business functions, oversees concurrent programs, and applies analytics to product, marketing, and credit risk decisions.

Leads enterprise AI engineering strategy and multi-team execution. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and production optimization. Makes technology decisions, scales reusable AI infrastructure and responsible AI practices, partners with research and risk teams, and develops engineering talent. Requires strong hands-on expertise in AI systems, cloud deployment, model optimization, and organizational leadership.

Conduct applied AI research and build foundation models from design through production deployment. Partner with data scientists, engineers, and product managers to develop scalable models, optimize training and inference, evaluate emerging methods, and translate research into customer-facing banking products. Lead research initiatives, define research agendas, mentor colleagues, and deliver production-scale solutions involving language, graph, image, or sequential data.

Build and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Own complex architectures, reliability SLOs, security and compliance reviews, technical roadmaps, and cross-functional delivery while mentoring engineering teams.

Designs, builds, deploys, and maintains production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI, governance, and security practices. The role collaborates with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.

Develop and deploy production machine learning models and infrastructure at scale. Responsibilities include model development, data pipeline construction, cloud and Kubernetes operations, distributed data processing, CI/CD automation, model monitoring and retraining, responsible AI governance, and collaboration with product and data science teams. The role requires strong programming, machine learning, distributed systems, and production deployment expertise.

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