Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 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 with ML design and ML infrastructure (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 working in a complex organization involving cross-functional, or cross-business projects.
- Track record of leading and delivering ML projects focused on on-device deployment (e.g., Android, iOS, web browsers, or embedded devices).
- Knowledge of ML converters/compilers and runtimes, and hardware-accelerated ML inference techniques.
- Understanding of Generative AI model architectures and their optimization for on-device execution.
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 deliver on-device ML infrastructure with leading performance, enabling framework and device optionality at scale. You will enable on-device deployment of key models, such as Gemini Nano and Gemma, across various accelerators (e.g., GPU / Pixel TPU / NPUs / CPU) on Android, Chrome, and more.
The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Create roadmaps for developer-facing Application Programming Interfaces (APIs), Software Development Kits (SDKs), and tools, ensuring they meet the evolving needs of Large Language Models (LLMs) workflows.
- Solve technically tests problems that exceed the scope of a generalist Software Engineers, specifically around optimizing Generative AI performance across heterogeneous hardware (CPUs, GPUs, and EdgeTPUs).
- Guide the team in designing resilient and robust systems, proactively anticipating scaling bottlenecks or shifts in usage as LLMs become increasingly complex.
- Coordinate efforts across multiple groups, including Android ML, ML Compiler, and DeepMind, to co-design performance and evaluation workflows.
- Provide technical mentorship, and implement new practices that address team needs and increase the velocity of your teammates.
