$200,000–$247,000
Lead GenAI Forward Deployed Engineer, YouTube GTM Operations
First seen · open on Google careers as of
$186,000–$269,000annualizedSan Bruno, CA +2
Lead the AI transformation of YouTube Business GTM operations by identifying operational bottlenecks and deploying enterprise-grade GenAI systems.
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At a glance
Posted base pay
$186K–$269KLevel with the median
Midpoint $228K · median $225K
Median $225K
This job$186K–$269KStaff+ FDE postings388 postings · middle 50% of posted pay$197K–$262KThe work
Builds for teams inside the company
Balance Mostly building
Project stages
- Pre-sales (not covered)
- Proof of concept
- Implementation
- Operations (not covered)
- Coding
- Writes production code
- Builds for
- Internal teams
- Level
- Staff+
- Role
- Technical lead
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The job description from Google
About the job
In gTech Users and Products (gUP), our mission is to advocate for Google’s users by creating helpful and trusted experiences across the product ecosystem. We achieve this by meeting partners and consumers where they are with support and help, representing their needs with our product partners and proposing fixes and features that elevate their engagement with Google's diverse product ecosystem. Additionally we provide a range of product services that ensure our products are optimized for every user, no matter where they are in the world (e.g., localization, digitization, partner integration, and more).
It's an exciting time to join YouTube's Go To Market Organization's AI Accelerator team, devising the AI transformation for YouTube Business GTM operations. The AI Accelerator is a newly formed task group dedicated to driving AI transformation across the organization. Our mission is to operate as a high-velocity, horizontal transformation engine, partnering directly with YouTube Biz 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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $186000 - $269000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Build GenAI PoCs (LLMs, RAG, agentic frameworks) to demonstrate feasibility to stakeholders, translating ambiguous business problems into software solutions.
- Serve as engineering Lead delivering complex AI applications, transitioning rapid prototypes to production-grade systems (e.g., multi-agent systems, MCP servers) that drive Return on Investment (ROI).
- Act as a trusted engineering partner to product managements and stakeholders, co-creating tool roadmaps that enable YouTube's business operations.
- Author technical designs, write clean/maintainable code, build front-ends, and deliver from scoping to deployment.
- Build high-performance eval pipelines and observability frameworks to resolve AI bottlenecks (data readiness, system integration, state-management) while ensuring accuracy, safety, compliance, and latency.
Qualifications
Minimum qualifications:
- Bachelor's degree in Computer Science, Electrical Engineering, Math, a related quantitative field, or equivalent practical experience in software development.
- 9 years of experience building APIs or web applications, designing scalable microservice or system architectures, including backend data pipelines, distributed systems, and data privacy.
- 8 years of experience in full-stack development using one or more programming languages (e.g., Python, Go, Java, C++, or TypeScript) with practical experience building enterprise-grade applied AI solutions.
- 4 years of experience working with teams and business stakeholders to create product roadmaps.
- 4 years of experience as a Tech Lead or Engineering Manager.
Preferred qualifications:
- Master’s degree or PhD in Artificial Intelligence, Computer Science, or a related field.
- 4 years of experience with SRE, InfoSec, or DevOps, with experience building LLM evaluation (evals) pipelines, observability, and optimizing LLM-native metrics at scale.
- 2 years of experience in vulnerability testing, security auditing, implementing AI safety guardrails, and ensuring enterprise accuracy.
- Experience with enterprise AI data infrastructure (vector databases, embedding generation, search architectures, data readiness, and state-management).
- Experience designing multi-agent systems (ReAct, tool-calling), MCP, and orchestration frameworks.
- Knowledge of GTM, sales workflows, or CRMs, with experience driving technical adoption of zero-to-one AI products.
About Google
Builds global consumer, ads, cloud, developer and AI platforms spanning Search, YouTube, Android, Workspace and Gemini.
Public · 5000+ people
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