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Software Engineering Manager, Search, Full Stack

Google

Company : Google

Location : Mountain View, CA, 94043

Posted Date : 23 October 2025

Job Details

Software Engineering Manager, Search, Full Stack

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. With your extensive technical expertise you take initiative to independently design and implement new systems, designing, implementing, and testing multiple features with little or no direction from tech lead or manager. You collaborate with key stakeholders to determine future direction of work.

In Google Search, we're reimagining what it means to search for information any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.

The US base salary range for this full-time position is $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Minimum Qualifications:

  • Bachelor's degree or equivalent practical experience.
  • 8 years of experience in software development.
  • 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  • 3 years of experience in a technical leadership role; overseeing projects.
  • 2 years of experience with GenAI techniques (e.g., Large Language Models, Multi-Modal, Large Vision Models) or with GenAI-related concepts (e.g., language modeling, computer vision).
  • 2 years of experience in a people management, supervision/team leadership role.

Preferred Qualifications:

  • Master's degree or PhD in Engineering, Computer Science, or a related technical field.
  • 3 years of experience working in a cross-functional organization.

Responsibilities:

  • Set and communicate team priorities that support the broader organization's goals. Align strategy, processes, and decision-making across teams.
  • Set clear expectations with individuals based on their level and role and aligned to the broader organization's goals. Meet regularly with individuals to discuss performance and development and provide feedback and coaching.
  • Develop the mid-term technical goal and roadmap within the scope of your team(s). Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
  • Design, guide and vet systems designs within the scope of the broader area, and write product or system development code to solve ambiguous problems.
  • Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).

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