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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

In this role, you will address the growing demand for comprehensive analysis and hardware, software co-design that enables cost effective peak performance and power optimization in future ML systems, as ML model and accelerator architectures continue to evolve. You will enable fast iteration and innovation for ML system co-design and optimization, while implementing automated hardware friendly model optimization and enablement at scale with deep understanding of the latest business-critical production ML models. Your work will involve full stack ML hardware, software co-design with engineering velocity and superhuman results, using objective and data-driven ground truth to drive innovations and inform ML platform roadmap.

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 $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

• Establish and maintain cross-organizational alignment on TPU hardware, TPU compiler, TPU systems hardware, software co-design simulator compiler technology for state-of-the-art tech stack, velocity, quality of results and verifiability.

• Develop compiler infrastructure for co-design simulator technologies to continuously keep up with evolving new system architecture choices and new ML workloads as well as supporting simulations at different abstraction levels.

• Explore and define future ML accelerator system and chip architecture with objective and data-driven ground truth while strategizing collaboration across partner organizations to maximize adoption and impact.

• Enable the cost effective peak performance of future ML systems with full-stack ML hardware, software co-design.

• Establish understanding of the latest business-critical ML models (large-language models, large embedding models etc.) to inform optimizations of model architecture, software systems and hardware architecture.

Minimum qualifications

• Bachelor's degree or equivalent practical experience.

• 8 years of experience in software development.

• 7 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.

• 5 years of experience with design and architecture; and testing/launching software products.

Preferred qualifications

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.

• Experience in constructing custom IR dialects and leveraging open-source compiler frameworks (MLIR, XLA) to solve mapping problems.

• Experience architecting custom lowering, transformations, and legalization passes specifically designed for rapid architecture exploration.

• Knowledge of computer architecture, TPU, LLM or other accelerators, while focusing on ML performance modeling and optimizations.