Forward Deployed Engineer, GenAI, YouTube
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Minimum qualifications:
- Bachelor's degree in Computer Science, Electrical Engineering, Mathematics or related quantitative field, or equivalent practical experience in software development.
- 4 years of experience in full-stack software development and system design, including delivering technical solutions from discovery to launch and communicating system designs to non-technical stakeholders.
- Experience with front-end languages (e.g., JavaScript or TypeScript).
- Experience with back-end languages (e.g., Python, C++, Java or Go) and building applied AI solutions/agentic workflows around pre-trained models.
- Experience working with database technologies (e.g., SQL, NoSQL), designing back-end data pipelines, and working with distributed systems.
Preferred qualifications:
- 4 years of experience with cloud, containerized, or microservice architectures, and building multi-step LLM, multi-agent, or Model Context Protocol (MCP) applications.
- 4 years of experience designing and optimizing databases, including vector databases, embedding generation, search architectures, and AI data-readiness.
- 2 years of experience with Site Reliability Engineering, Information Security, or DevOps practices.
- 2 years of experience collaborating with stakeholders on GTM operations, enterprise sales workflows, or CRM/CMS integrations.
- Experience in consulting or entrepreneurial environments, delivering zero-to-one initiatives and building scalable products from the ground up.
About the job
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 dedicated to 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 will be 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.
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: $128000 - $185000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Build and iterate on proof-of-concepts (PoCs) using frontier GenAI (Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic Frameworks) to demonstrate technical feasibility to stakeholders, translating ambiguous business problems into tangible software solutions.
- Serve as the engineering point of contact for delivering complex AI applications, transitioning from rapid prototypes to production-grade, AI-powered solutions (e.g., multi-agent systems, model context protocol (MCP) servers) that drive measurable Return on Investment (ROI).
- Author technical design documents and project plans; write, test, and ship clean, maintainable code; build intuitive front-end surfaces; and execute delivery from initial scoping through to deployment.
- Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, compliance and latency.
- Identify and resolve technical hurdles that prevent AI from reaching maturity, including data readiness gaps, system integration complexities, edge-case handling, and challenges.
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