Temporary Google Sales Engineer Jobs in Virginia

Minimum qualifications:

• Bachelor's degree in Computer Science, Engineering, a related field, or equivalent practical experience.
• 8 years of experience as a sales engineer or technical consultant in a cloud computing environment or in a customer-facing role.
• 5 years of experience managing a Software Engineering, Forward Deployed Engineering (FDE) or a similar direct technical customer-facing team in a cloud computing environment.
• Experience in Python or similar coding languages.
• Experience developing AI/GenAI solutions utilizing AI tools, or designing multi-agent workflows or RAG systems.
• Must possess an active Top Secret/SCI Security Clearance.

Preferred qualifications:

• Master's degree or PhD in AI, Computer Science, or a related technical field.
• Experience in architecting AI solutions within complex infrastructures, ensuring data sovereignty and secure governance.
• Experience either working within or managing across the Public Sector space (Federal and SLED markets).
• Expertise in designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability to foster user trust.
• Ability to design end-to-end secure, observable multi-agent systems using complex design patterns (ReAct, self-reflection,etc), state management, and tool-calling protocols.

About the job
The Google Public Sector Forward Deployed Engineering (GPS FDE) team is a squad of direct "innovator-builders" who rapidly deploy production-grade, secure AI solutions across Federal and SLED environments. Operating with a high-agency startup mindset, our engineers don't just advise; they actively code, debug, and co-build bespoke agentic workflows directly alongside our customers. We resolve complex integration, data sovereignty, and security challenges within strict compliance frameworks, utilizing talent with Top Secret/Sensitive Compartmented Information (TS/SCI) clearances. Ultimately, the GPS FDE team accelerates the safe, reliable adoption of generative AI across mission-critical operations while feeding field insights directly back to Google Cloud Product engineering.
As a Manager of a GenAI Forward Deployed Engineering (FDE) team, you will lead a squad of AI/ML engineers across the Public Sector (SLED and Federal markets) to bridge the gap between frontier AI products and production-grade reality within customers. You will be responsible for a team that doesn't just consult, but codes, debugs and jointly deploys bespoke agentic solutions directly within customer environments.
In this role, you will provide in-depth search technical mentorship to your team while balancing high-level alignment with Product, Engineering, and Google Cloud Regional Sales leadership. You will empower and unblock your team as they resolve production-level obstacles, including data readiness issues, integration complexities, and state-management issues that hinder AI from achieving enterprise-grade maturity.
Google Public Sector brings the magic of Google to the mission of government and education with solutions purpose-built for enterprises. We focus on helping United States public sector institutions accelerate their digital transformations, and we continue to make significant investments and grow our team to meet the complex needs of local, state and federal government and educational institutions.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $262000 - $364000 (USD) 25% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities

• Serve as the ultimate technical lead, establishing code standards, architectural best practices, and benchmarks to elevate engineering excellence across the team.
• Partner with Sales and Tech Leadership to define requirements for high-value opportunities, deploying specialized experts (MLOps, GenMedia, or Agentic systems) to key accounts.
• Lead technical hiring for FDE, evaluating AI/ML expertise, systems engineering, and direct coding skills to build an engineering squad.
• Identify skill gaps in emerging tech (MCP, tool-calling, and foundation models), ensuring the team maintains subject matter expertise in an evolving AI stack.
• Collaborate with Product and Engineering to resolve blockers and translate field insights into roadmaps while building internal tools to drive organizational efficiency.

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