Minimum qualifications:

  • Master's degree in Computer Science, in a Machine Learning related technical field, or equivalent practical experience.
  • 3 years of experience with coding in one or more of the following languages: C, C++, or Python.
  • Ability to speak and write in English fluently.

Preferred qualifications:

  • PhD in Computer Science and/or a Machine Learning related technical field.
  • Experience in model optimization (e.g., pruning, quantization, and knowledge distillation) and meta-learning.
  • Experience in building, deploying, and improving Machine Learning models and algorithms in real-world products.
  • Proficiency in building and deploying machine learning models (e.g., TensorFlow, PyTorch).

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.

TensorFlow is Google's machine learning framework and at the heart of our transformation into an AI first company. Besides being a crucial component of Google's machine learning strategy, TensorFlow is also a successful open-source project.

TensorFlow Model Optimization Toolkit is a suite of tools that implements techniques for optimizing machine learning models for deployment and execution. The toolkit is part of TensorFlow endorsed APIs.

A common but non-exhaustive list of motivating use cases includes: reducing latency and cost for inference for both cloud and mobile devices; deploying models on edge devices with restrictions on processing, memory and/or power-consumption; reducing payload size for over-the-air model updates; enabling execution on hardware restricted-to or optimized-for a restricted set of instructions; optimizing models for special purpose hardware accelerators.

Google is an engineering company at heart. We hire people with a broad set of technical skills who are ready to take on some of technology's greatest challenges and make an impact on users around the world. At Google, engineers not only revolutionize search, they routinely work on scalability and storage solutions, large-scale applications and entirely new platforms for developers around the world. From Google Ads to Chrome, Android to YouTube, social to local, Google engineers are changing the world one technological achievement after another.

Responsibilities

  • Design, develop, test, deploy, maintain, and improve ML framework and infrastructure, as well as optimizing models for production deployment.
  • Manage individual project priorities, deadlines, and deliverables.
  • Work closely with other engineering teams to reuse and understand existing frameworks.