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

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 an organization involving cross-functional or cross-business projects.

About the job

Software Engineering Managers provide technical leadership for major projects while managing teams of engineers. They manage project goals, contribute to product strategy, develop their teams, and help engineers optimize their code.

This role manages engineers across multiple teams and locations, a large product budget, and the deployment of large-scale projects across multiple international sites. The Google Cloud AI Research team addresses AI challenges across industries including technology, healthcare, finance, and retail, conducting research with scientific and real-world impact and collaborating with product teams to bring innovations to customers.

Responsibilities

• Set and communicate team priorities that support broader organizational goals; align strategy, processes, and decision-making across teams.
• Set clear expectations based on individuals’ levels and roles; meet regularly to discuss performance and development, and provide feedback and coaching.
• Develop the mid-term technical vision and roadmap for one or more teams; evolve the roadmap for future requirements and infrastructure needs.
• Design, guide, and vet system designs; write product or system development code to solve ambiguous problems.
• Lead the design of GenAI solutions, optimize ML infrastructure, and guide data preparation and model optimization strategies.