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 People Operations Engineering team is dedicated to building innovative and scalable technology solutions that empower Google's global workforce. We leverage the power of data and AI/ML to optimize HR processes, enhance employee experiences, and drive organizational efficiency.

You will be a key leader, responsible for the strategic direction, technical outlook, and overall success of multiple teams focused on developing and deploying impactful AI-powered solutions for Google's employees. You will manage engineering managers and/or lead larger, more complex teams, fostering a culture of innovation, technical excellence, and significant impact.

The US base salary range for this full-time position is $262,000-$365,000 + 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.

Responsibilities

• Provide strong technical leadership and strategic direction for a significant People Operations Engineering portfolio, aligning with AI/ML strategies.

• Manage and mentor engineering managers and large, complex teams, fostering a high-performing culture, setting direction, and driving career growth.

• Partner with executive product leaders and HR stakeholders to shape long-term roadmaps, influence strategy, and translate business objectives into impactful AI/ML solutions.

• Oversee the design, development, and deployment of scalable, reliable, and secure AI-powered HR systems, advocating software and Machine Learning Operations (MLOps) best practices.

• Identify and mitigate significant technical risks, drive organizational efficiency, and advocate the adoption of AI/ML to transform human resources at Google.

Minimum qualifications

• Bachelor's degree or equivalent practical experience.

• 8 years of experience in software development.

• 7 years of experience with full stack development, across the back-end such as Java, Python, GO, or C++ codebases, and front-end experience including JavaScript or TypeScript, HTML, CSS, or equivalent.

• 5 years of experience in a people management or team leadership role.

• 5 years of experience in a technical leadership role.

Preferred qualifications

• Experience architecting and managing data pipelines using large-scale data engineering tools (e.g., Beam, Dataflow, Big Query).

• Experience designing, developing, and deploying large-scale AI/ML systems in a cloud environment, with a focus on Google Cloud Platform (GCP).

• Experience with specific AI/ML applications in the HR domain (e.g., advanced NLP for understanding employee feedback, sophisticated predictive models for talent management, personalized learning platforms).

• Understanding of the wide range of machine learning algorithms, statistical modeling techniques, and data analysis methodologies relevant to HR data and challenges.

• Ability to build and lead high-performing engineering teams.