Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Design, build, and secure scalable infrastructure and data ingestion pipelines for an enterprise AI platform handling sensitive financial data. Implement private-cloud, tenant-isolated deployments, access controls, monitoring/alerting, CI/CD, and infrastructure-as-code. Partner with AI research/product teams to optimize for LLM training and inference workloads.

Build Rowspace's marketing function from scratch, owning ABM, demand gen, events, content, GTM infrastructure, and stack implementation. Target and tier accounts, develop personalized outreach, run executive events, craft content and social strategy, align closely with sales and founders, and feedback market insights to product and engineering.

Own post-sale customer relationships for Rowspace: drive onboarding, adoption, expansion, and retention. Translate customer workflows into product feedback, build engagement playbooks, and support sales for expansions.

Founding Enterprise Account Executive responsible for full sales cycle—sourcing, discovery, demos, and closing—while building sales processes, refining ICP, sharing market feedback with product, supporting post-close handoffs, and hiring/mentoring future AEs.

Lead full business development cycle targeting institutional investors, shape GTM strategy, develop account plans, synthesize customer and product signals, drive cross-functional initiatives, and help hire and scale the GTM team.

Lead full-cycle recruiting for technical and non-technical roles: build talent pipelines and processes, source and close candidates, partner with hiring managers to design job descriptions and interview processes, leverage AI and recruiting tools to automate workflows, and help shape employer brand.

Build full-stack product features and ingestion pipelines to convert messy investment data into a unified knowledge engine. Design scalable data models and APIs, ship UI and custom agent workflows, maintain security and reliability for sensitive investment data, engage directly with customers, and create evaluation sets for LLM systems.

Embed with customers to build integrations and data pipelines, debug retrieval quality and LLM performance in production, scope and ship new workflows, partner with deployment managers to drive adoption, and translate customer needs into product improvements and scalable playbooks.

Build and productionize applied AI systems for finance: design retrieval pipelines, knowledge graphs, agentic workflows, evaluation frameworks, and LLM orchestration. Iterate with customer feedback, ensure traceability to sources, and deploy/operate reliable production systems across the stack.