Senior Staff Software Engineer, Google Cloud AI
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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 optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience with design and architecture and testing/launching software products.
- 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.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures/algorithms.
- 5 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
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.
New Product Introduction (NPI) and Platform Readiness team within AI Hypercomputer Infrasturctue organization helps bring new Tensor Processing Unit (TPU) and Graphics Processing Unit (GPU) generations to the Google Cloud platform and enables end-to-end AI/ML compute experience for Google Cloud users. We help bring up and qualify the complete Cloud TPU and Cloud GPU Hypercomputer stack, including VMs, Networking, Storage, Google Kubernetes Engine (GKE) and software tool chains, etc.
We build and deliver the telemetry infrastructure for both the GPU/TPU fleet as well as the critical AI/ML workloads. We optimize the stability and performance for high priority AI/ML workloads across the stack. We also do model benchmarking and optimizations for standard models during the NPI process.
The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.Responsibilities
- Build and lead strategic technical alignment with major organizations across Google (PIE, GCE, GKE, GCS, etc.) to accelerate the velocity of bringing the Machine Learning Hardware to Google Cloud with dynamic and engaged Time to Mitigate (TTM) goals.
- Use your extensive expertise in distributed systems and machine learning to develop and execute multi-year plans for validating end to end stack for TPU and GPU Products.
- Ensure that our products deliver performance and stability to make our AI/ML customers successful and meet the demands of the growing business.
- Develop strong collaborative relationships across organizational boundaries with cross-functional teams to achieve the delivery of a high performing
end to end stack with components delivered by teams across these organization boundaries. - Provide technical leadership and mentorship to the team as an overall Über Tech Lead (UTL).
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