Software Engineer, AICore, Knowledge and Information

1 Month ago • 2 Years + • Full Stack Development • Artificial Intelligence

Job Summary

Job Description

This Software Engineer role at Google focuses on adapting and implementing the Gemini model for mobile applications, optimizing the AICore runtime (Gemini Nano) for Android, building developer APIs for Android app innovation, and prototyping novel user experiences using RAG, traditional ML models, LoRAs, or LLM fine-tuning. The position requires collaboration with Google DeepMind and CoreML teams. Responsibilities include optimizing model integration into the mobile environment and empowering app developers to create innovative experiences. The ideal candidate will have experience in software development (C++, Java, Python), data structures, algorithms, and ideally, machine learning infrastructure, mobile development, and generative AI.
Must have:
  • Bachelor's degree or equivalent experience
  • 2 years software development experience (or 1 year with advanced degree)
  • 2 years experience with data structures/algorithms
  • Collaborate with Google DeepMind and CoreML
  • Optimize Gemini Nano for mobile
  • Build developer-facing APIs
Good to have:
  • Master's/PhD in CS or related field
  • Experience with ML infrastructure
  • Mobile development experience
  • Generative AI experience

Job Details


Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 2 years of experience with software development in one or more programming languages (e.g., C++, Java, Python), or 1 year of experience with an advanced degree.
  • 2 years of experience with data structures or algorithms.

Preferred qualifications:

  • Master's degree or PhD in Computer Science or related technical fields.
  • Experience with Machine Learning infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
  • Experience with Mobile Development.
  • Experience with Generative AI.

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's mission is to organize the world's information and make it universally accessible and useful. Our Devices & Services team combines the best of Google AI, Software, and Hardware to create radically helpful experiences for users. We research, design, and develop new technologies and hardware to make our user's interaction with computing faster, seamless, and more powerful. Whether finding new ways to capture and sense the world around us, advancing form factors, or improving interaction methods, the Devices & Services team is making people's lives better through technology.

Responsibilities

  • Collaborate with Google DeepMind, CoreML to adapt and implement the Gemini model for mobile user applications.
  • Optimize the AICore runtime, specifically Gemini Nano, ensuring they are optimally integrated into the mobile environment across the Android ecosystem.
  • Build developer facing API to empower app developers to build innovated user experiences in Android applications.
  • Engage in prototyping with product teams, aiming to pioneer novel user experiences by elevating mobile capabilities through methods such as RAG (Retrieval-Augmented Generation), traditional Machine Learning models, LoRAs or targeted LLM fine-tuning.

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