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Home›Jobs›Google›Software Engineering Manager II, AI/ML GenAI, Google Cloud
Google

About Google

Empowering the world through technology and information

🏢 Tech👥 100K+📅 Founded 1998📍 Mountain View, California, United States

Key Highlights

  • Over 100,000 employees globally
  • Headquartered in Mountain View, California
  • Parent company Alphabet Inc. valued at $1.5 trillion
  • Google Cloud Platform serves millions of customers

Google LLC, headquartered in Mountain View, California, is a global leader in internet-related services and products, including its flagship search engine, Google Search, and the Android operating system. With over 100,000 employees, Google also offers cloud computing services through Google Cloud P...

🎁 Benefits

Google offers competitive salaries, equity options, generous PTO policies, comprehensive health benefits, and a remote work policy that allows flexibi...

🌟 Culture

Google is known for its engineering-first culture, emphasizing innovation and collaboration. The company fosters a unique environment that encourages ...

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Google

Software Engineering Manager II, AI/ML GenAI, Google Cloud

Google • Raleigh, NC, USA, Durham, NC, USA

Posted 3d ago🏛️ On-SiteSeniorLeadSoftware engineering manager📍 Raleigh📍 Durham
Apply Now →

Skills & Technologies

PythonCC++JavaJavaScriptMachine learningModel deploymentModel evaluationData processingDebugging

✨ AI Summary

Google is seeking a Software Engineering Manager II for their AI/ML GenAI team to lead software development and optimize ML infrastructure. You'll manage a team while working with programming languages like Python, C++, and Java, and applying machine learning techniques. This role requires significant experience in technical leadership and ML design.

Job Description

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
  • 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.
  • 2 years of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
  • 2 years of experience in a people management or 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 complex, matrixed organization involving cross-functional, or cross-business projects.

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

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.
  • 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 vision and roadmap within the scope of your (often multiple) 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.
  • Lead the design of GenAI solutions, optimize ML infrastructure, and guide the development of data preparation and model optimization strategies.

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