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

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

Lead modernization of the Salesforce ecosystem by optimizing development practices, reducing technical debt, improving performance and integrations, implementing best practices and governance (focus on Service Cloud), driving version control adoption, and providing technical leadership and training to create scalable, cost-effective solutions.

Design and implement high-performance Angular frontends for AI-driven interfaces: real-time SSE/WebSocket streaming, complex state with Signals/NgRx, Chain-of-Thought visualizations, human-in-the-loop controls, performance optimization, API schema collaboration, and testing.

Build and maintain automated test suites and perform manual testing to ensure product quality. Train squads on test management, improve test coverage, troubleshoot data issues, and promote strong quality practices within Agile teams.

Design visually compelling digital interfaces and marketing-driven user experiences. Produce wireframes, high-fidelity mockups, and interactive prototypes. Apply and extend design systems, collaborate with creative, marketing, product, and development teams, present design rationale, and iterate quickly in fast-paced marketing cycles.

Perform hands-on data governance: identify root causes using information management tools and data sources, collaborate with data owners and architects, define governance rules and values, embed governance tests into ETL, engage vendors and stakeholders, and establish/report on data governance metrics and continuous improvements.

Design, build, test, and maintain high-performance Python data applications and backend services. Lead technical design, review code, mentor junior engineers, integrate with databases and cloud services, optimize performance, and ensure security, scalability, and reliability.

Design, build, and deploy production-grade ML and AI systems end-to-end, combining classical ML, deep learning, and LLM/agentic integrations. Implement MLOps, monitoring, CI/CD, model serving, vector search/RAG pipelines, and scalable APIs; mentor engineers and drive engineering best practices.

Design, build, and execute automated tests for native mobile and WebView user journeys, APIs, and integrations. Perform performance and reliability testing for critical APIs and transactions, run tests on cloud device farms, analyze failures across devices/OS versions, participate in sprint planning, test design, defect triage, and collaborate with developers to resolve quality issues early.

Lead architecture, design, and delivery of large-scale microservices systems. Define standards, mentor teams, drive cloud, containerization, CI/CD, observability, data strategies, and risk mitigation while aligning technology with business goals.