Software Engineering Manager, Applied Machine Learning, Google Workspace

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).
  
• 3 years of experience in a technical leadership role overseeing projects, with 2 years of experience in a people management, supervision/team leadership role.
  
• Experience with Machine Learning or Generative AI concepts (e.g., Large Language Models, diffusion models, etc.), development workflows, and use cases.
  

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• 3 years of experience with machine learning algorithms and tools (e.g., TensorFlow, PyTorch), or applied ML (e.g., deep learning, LLMs, Multimodal models).
  
• 3 years of experience working in an organization.
  
• Experience leading, designing, and implementing distributed systems.
  
• Experience working on consumer-facing products.
  

About the job

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 product team works at the intersection of Video, Machine Learning, and Generative AI to explore new product ideas, engage with Research teams, and solve user problems.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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.

Responsibilities

• Set and communicate team priorities that support the broader organization's goals, and align strategy, processes, and decision-making across teams.
• Set expectations with individuals based on their level and role, meet regularly with individuals to discuss performance and development, and provide feedback and coaching.
• Develop the mid-term technical idea and roadmap within the scope of the team, and evolve the roadmap to meet anticipated future requirements and infrastructure needs.
• Design and guide systems designs within the scope of the broader area, and write product or system development code to solve 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).