Staff Software Engineer, On-Device Machine Learning
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience with software development in C++ programming language.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience leading ML design and optimizing end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 3 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.
- Experience in deploying on-device Machine Learning models.
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.
Google AI Edge enables developers and Google products to deploy AI across mobile, web and embedded with with our AI edge stack (ai.google.dev/edge) - from low-code APIs to hardware specific acceleration libraries to achieve leading performance and device optionality at scale. Our team focuses on cross-platform infrastructure and solutions serving all of Google AI's biz needs.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving team behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Enable one or more areas in Google AI Edge Developer Tools such as AI Edge Gallery (Gemma's showcase app for on-device Gemma 0-day), Developer Device Platform (Device farm with AI, enabling developers to test/eval/deploy apps and models across device), and AI Edge Eval (innovative eval framework for on-device model and harness).
- Train task-specific models of in-app AI enabling Gemma model/runtime co-design (quantization, conversion), build industry-leading on-device AI solutions (voice translate, generative image editing)
- Enable tooling of GTM for on-device Gemma 0-day (AI Edge Gallery), helping developers to test/eval/deploy across devices.
- Enable on-device deployment of key models across various accelerators (GPU/Pixel TPU/NPUs/CPU) on Android, Chrome, and more.
- Improve performance of on-device model inference via optimizations in the model presentation, on-device runtime and kernel implementation.
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