Senior Database Engineer Jobs in Valencia, CA (NOW HIRING)

• Bachelor's degree in Computer Science, Electrical Engineering, Mathematics or related quantitative field, or equivalent practical experience in software development.
• 6 years of experience in full-stack software development and system design.
• Experience with front-end languages (e.g., JavaScript or TypeScript).
• Experience with back-end languages (e.g., Java, Python, Go or C++) and building applied AI solutions/agentic workflows around pre-trained models.
• Experience working with database technologies (e.g., SQL, NoSQL), distributed systems, and designing back-end data pipelines.

• 2 years of experience as a Technical Lead or Engineering Manager, including zero-to-one delivery and scoping in ambiguous environments.
• 2 years of experience with Site Reliability Engineering, Information Security, or Developer Operation practices, with practical enterprise AI experience (LLM evals, observability, safety).
• Experience building advanced GenAI (multi-step LLM, multi-agent systems, or MCP integrated with orchestration frameworks).
• Experience with AI data infrastructure: vector databases, embedding generation, search architectures, and state-management challenges.
• Experience integrating tools with enterprise business systems and CRMs.
• Knowledge of GTM/sales workflows.

It's an exciting time to join GTMO's AI Accelerator team, devising the AI transformation for YouTube Business GTM operations. The AI Accelerator is driving AI transformation across the organization. Our mission is to operate as a high-velocity, horizontal transformation engine, partnering directly with YouTube Business GTM business domains to fundamentally redesign legacy workflows from scratch and leverage applied AI to drive direct business impact for YouTube. We operate at the intersection of consulting, product strategy, applied AI and systems engineering. We identify the most painful operational bottlenecks across the organization and rapidly deploy intelligent, enterprise-grade AI powered solutions to solve them.

As an AI Forward Deployed Engineer (FDE) within the YouTube Business organization, you are an entrepreneurial, full-stack builder tasked with influencing the future of YouTube’s Go-To-Market (GTM) operations. Operating as a builder-consultant, you will bridge the critical gap between frontier AI capabilities and production-grade reality. You will actively code, debug, and ship AI powered and agentic solutions that solve complex business problems.

You will deliver scalable solutions that seamlessly connect intuitive frontend user experiences with complex backends and data pipelines. By integrating next-generation AI (LLMs, RAG, and agentic workflows) into GTM operations, you will systematically address the core blockers to enterprise AI maturity, such as integration complexities, data readiness issues, and state-management challenges. If being at the intersection of engineering, GenAI, and product strategy excites you, and you are ready to deliver solutions that have an immediate, outsized impact on YouTube’s business workflows, this is the role for you.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $155000 - $224000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

• Build and iterate on GenAI proof-of-concepts (Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic Frameworks) to demonstrate feasibility, translating business problems into software.
• Lead delivery of complex, production-grade AI solutions (e.g., multi-agent systems, model context protocol servers) from rapid prototypes to maximize business Return on Investment (ROI).
• Partner with Product Managers and stakeholders to co-create tool roadmaps that enable YouTube's business operations.
• Author technical designs, write clean code, build intuitive frontends, define metrics, and execute deployment. Build high-performance eval pipelines and observability frameworks to ensure agentic systems meet accuracy, safety, and latency requirements.
• Resolve technical hurdles preventing AI maturity, including data readiness gaps, system integration complexities, edge cases, and state-management challenges.