Senior Staff Software Engineer, GenAI, Performance Engineering

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 7 years of experience leading technical project strategy, ML design, and working with industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
  
• 5 years of experience with design and architecture, and testing/launching software products.

Preferred qualifications:

• Experience with Machine Learning, Distributed Computing, Build Infrastructure, and Cluster Management.
• Experience with C++, Python, Machine Learning Infrastructure, Compilers, Computer Architecture, Debugging, etc.
• Experience in a technical leadership role, with the ability to lead complex, cross-functional engineering projects from conception to completion.

About the job

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.

The Machine Learning (ML) Strategy and Allocation Committee (MLSA) Technical Core Team is a central technical body responsible for shaping the future of Google's AI by optimizing the vast ML compute resources. As Google's investment in AI accelerates, the efficient allocation, strategic alignment, and technical governance of the ML fleet are paramount to the success.

In this role, you will develop the frameworks, policies, and technical strategies that ensure the ML compute capacity is aligned with Google's strategic priorities, used with maximum efficiency, and operates as a consistent, exceptional system. Your work spans the entire ML domain, taking on complex challenges in infrastructure efficiency, technical governance, and resource allocation at a scale that is unprecedented in the industry.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

The US base salary range for this full-time position is $248,000-$349,000 + 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

• Lead technical analysis across Google’s ML infrastructure (including training, serving, and scheduling) to identify opportunities for efficiency gains, cost optimization, and improved resource utilization. Develop data-driven proposals and recommendations for executive leadership.
• Partner with executive technical leads across serving, training, scheduling, and fleet management to establish and drive technical governance. 
• Collaborate with MLSA leadership to translate Google's strategic AI priorities into a concrete technical roadmap for the ML compute resources, ensuring the capacity planning and allocation strategies support critical initiatives.
• Serve as a key technical consultant and guide for Product Areas (PAs) and engineering organizations. Help them navigate MLSA policies, optimize their capacity consumption, and align their roadmaps with Google’s overall ML strategy.
• Develop and advocate technical proposals for new frameworks, tools, and systems that enable more efficient and dynamic allocation of ML resources.