Senior Software Engineering Manager, TPU Performance
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience with software development.
- 7 years of experience leading technical project strategy, ML design, and optimizing 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 in a technical leadership role; overseeing projects.
- 5 years of experience in a people management, supervision/team leadership role.
Preferred qualifications:
- Experience focused on ML performance modeling and Improvements.
- Experience on Large-Language Models (LLMs), ML framework and compiler.
- Knowledge of performance analysis and experience in performance modeling of High-Performance Computing (HPC) interconnect topologies.
- Knowledge of computer architecture (Tensor Processing Unit (TPU) or other accelerators).
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.
With the increasing complexity of ML model and ML accelerator architectures, there is a demand for comprehensive analysis and HW/SW Co-Design that can enable cost effective performance and power of future ML systems such as fast iteration and innovation for ML system co-design and improvement, automated HW-friendly model improvement/enablement at scale, understanding on latest business-critical production ML models and full stack ML hardware/software co-design with 10-100X engineering velocity and results, objective and data-driven ground truth to drive innovations and inform ML platform roadmap, performance analysis to inform next-generation ML system and chip architecture using full stack HW-SW design space exploration.
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.
Responsibilities
- Explore and define future Machine Learning (ML) accelerator system and chip architecture with objective and data-driven ground truth.
- Enable the cost effective peak performance of future ML systems with full stack ML Hardware/Software (HW/SW) co-design.
- Establish understanding of the latest business-critical production ML models (Large-language models, large embedding models etc.) to inform improvements of model architecture, software system and hardware architecture.
- Develop 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.
- Manage a team of 7 (eventually growing to 15) people.
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