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

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.

This role involves developing core payment services, designing robust ledgers, ensuring high-scale reliability, and maintaining compliance in a fast-paced environment.

The Senior Backend Engineer will build and operate backend systems for AI agents, focusing on reliability, API design, and data modeling, while enhancing code quality and performance in a fast-paced environment.

Lead end-to-end product for Vantaca's core management platform: run deep customer discovery, design API-first features and agentic AI workflows, partner with engineering and design, define success metrics, and drive adoption and measurable customer outcomes across discovery, spec, build, and launch.

The Senior Engineer will build and operate backend systems for AI agents, design APIs, optimize performance, and uphold engineering standards to ensure platform reliability and scalability.

The Forward Deployed Engineer will design, build, and deploy AI agent solutions for customers, optimize workflows, and enhance automation capabilities through active client engagement and collaboration with engineering teams.

The Sr. Applied AI Engineer will design, build, and deploy production-grade ML and LLM systems. Responsibilities include collaborating with various teams, developing evaluation infrastructure, maintaining versioned LLM workflows, and ensuring data governance.

Build and operate the data platform powering Vantaca IQ: ingestion and CDC pipelines, data modeling, aggregations and materializations, multi-tenant isolation, query/metrics APIs and reporting surfaces, LLM grounding/eval harnesses, and governed semantic layers to serve humans and agents with trustworthy intelligence.

Design, build, and maintain cloud architecture, model serving infrastructure, and ML pipelines. Optimize AI workloads for low-latency inference and cost efficiency, implement CI/CD for model deployment, monitoring, and versioning, and ensure reliability, security, and observability for mission-critical AI services. Collaborate with product and engineering teams, document runbooks, and mentor colleagues.