Top AI & Machine Learning Jobs in San Francisco, CA

Drive customer-facing AI and cloud solutions: acquire and prepare data, build and evaluate scalable models, write extensible code, research industry trends, define solution strategy, and collaborate with cross-functional teams and customers to deliver high-impact engineering projects.

Lead sales for Cloud and AI platform solutions, engage and qualify enterprise customers, drive co-selling with partners, manage pipeline and forecasting, and advise customers on AI-driven modernization and secure cloud adoption to accelerate consumption and renewals.

Design, build, and optimize high-performance, low-latency networking infrastructure for large-scale AI training on Azure. Benchmark and improve scalability and reliability, debug complex network issues, write and refactor code, author design documents, act as DRI and on-call, and drive observability and performance improvements for distributed systems.

Lead customer-facing Azure Cloud & AI architecture engagements: advise executives, design secure scalable solutions, drive Azure adoption, own technical delivery, scale AI solutions, mentor peers, and collaborate with partners to execute cloud transformation.

Customer-facing Cloud Solution Architect responsible for designing, delivering, and operationalizing AI-powered applications on Azure. Lead architecture, POCs, MVPs, and production readiness for Azure AI, AKS, App Service, APIM, Serverless, Containers, and GitHub. Drive customer adoption, consumption growth, resiliency, security, and optimization while providing technical workshops, mentoring, and feedback to engineering teams.

Customer-facing cloud solution architect who designs and implements AI-enabled business process solutions using Dynamics 365, Power Platform, and Copilot Studio. Lead architecture, proof-of-concept, migrations from legacy systems, performance and cost optimization, and operational health engagements while advising customers and partners to drive adoption and business value.

Design, build, and scale production-grade AI applications and agent-based systems. Lead customer technical engagements, architect end-to-end AI solutions, create reusable accelerators, influence product roadmap, and operationalize agentic software engineering with CI/CD, MLOps/LLMOps, observability, and security.