Staff Software Engineer, On-Device Machine Learning

About the role

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.

We serve Google's flagship apps across Android, iOS and Web with infrastructure,

models and solutions (YouTube Shorts/Meet/Photos/Translate/Messages, etc.). We

enable GDM with 0-day Gemma launches (Gemma 3n and 4 and variants such as

EmbeddingGemma), and support our platforms (Chrome/Android) and Devices

(Pixel/Watch). We also enable third-party developers and the ecosystem.

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

[https://www.google.com/about/careers/applications/benefits/].

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.