Top Tech Jobs & Startup Jobs in San Francisco, CA

Leads an enterprise data science team developing agentic AI products and machine learning solutions. Responsibilities include defining AI strategy, designing experiments and models, evaluating and validating modeling systems, analyzing telemetry, building benchmark and challenger models, and advising senior business and regulatory stakeholders. The role requires extensive experience with large-scale data analysis, machine learning, relational databases, open-source programming languages, cloud platforms, software architecture, and DevOps practices.

Leads cloud asset and inventory intelligence initiatives, defines technical vision and platform strategy, and architects scalable, secure cloud-native systems. Drives modernization toward microservices and event-driven platforms, establishes API and design standards, and applies AI and automation to improve engineering workflows. Collaborates across divisions, leads complex projects, mentors engineers and technical leaders, influences technology strategy, and ensures performance, reliability, security, and operational excellence across multi-cloud environments.

Leads enterprise product management by identifying customer needs, translating business strategy into digital products, driving technology-enabled innovation, solving complex problems, mitigating product risks, and aligning cross-functional teams toward transformational outcomes.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize distributed AI infrastructure for cost, latency, throughput, scalability, and hardware utilization. Own complex system architectures, define reliability objectives, lead technical reviews, ensure security and compliance, shape long-term platform strategy, and mentor senior technical associates.

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Define technical roadmaps and standards, lead architecture and governance reviews, integrate heterogeneous AI components, and mentor senior engineering talent.

Lead enterprise-scale technical programs for AI and ML platforms, guiding initiatives from research and concept through production. Partner with researchers, engineers, product, data science, and business leaders to manage technical risks, make technology decisions, deliver high-reliability systems, and achieve business outcomes. Build and expand the technical program management practice, mentor TPMs, drive cross-functional execution, and support large-scale distributed, cloud-based, real-time data solutions.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance reviews, optimize models and infrastructure for latency, throughput, scalability, and cost, establish AI engineering standards, and mentor senior technical teams.

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

Design, develop, deploy, and support scalable generative AI systems, including foundation models, LLM inference, agentic and multi-agent workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, and hardware utilization. Shape technical roadmaps, lead design reviews and governance councils, integrate heterogeneous AI pipelines, and mentor senior AI engineers while applying responsible AI standards.

Design, develop, deploy, and support scalable AI systems involving foundation models, LLM inference, agentic workflows, guardrails, evaluation, similarity search, governance, and observability. Optimize training and inference for cost, latency, throughput, and hardware utilization. Define reliability objectives, lead architectural reviews, ensure security and compliance, contribute to long-term AI platform roadmaps, and mentor engineering staff.