AICore Software Engineer

1 Month ago • 5-8 Years • Full Stack Development • Research & Development • Artificial Intelligence

Job Summary

Job Description

As an AICore Software Engineer at Google, you will collaborate with AI experts to adapt and deploy the Gemini model for mobile applications. You'll optimize the AICore runtime, focusing on Gemini Nano for Android. Responsibilities include designing and building user-friendly developer APIs and working with product teams to implement innovative AI-powered user experiences using techniques like RAG, traditional machine learning, LoRAs, and LLM fine-tuning. The role requires significant software development experience, expertise in data structures/algorithms, and a strong understanding of AI/ML principles. This position is critical to Google's efforts in developing next-generation technologies for billions of users.
Must have:
  • 5+ years software development experience
  • Experience with data structures/algorithms
  • 3+ years software product development
  • 1+ year software design & architecture
  • Collaborate with AI experts
  • Optimize AICore runtime
  • Build developer-facing APIs
  • Implement innovative AI user experiences
Good to have:
  • Master's/PhD in CS
  • Mobile or embedded systems experience
  • Machine learning feature development
  • Experience with Gemini Nano

Job Details


Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 5 years of experience with software development in one or more programming languages, and with data structures/algorithms.
  • 3 years of experience in developing software products, and 1 year of experience with software design and architecture.

Preferred qualifications:

  • Master's degree or PhD in Computer Science or related technical field.
  • Experience with mobile development or embedded system.
  • Experience with Machine Learning powered feature development and productization.

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 directly with AI experts from Google DeepMind and CoreML to adapt and deploy the Gemini model for seamless integration into mobile user applications.
  • Optimize the AICore runtime, with a focus on Gemini Nano, to ensure optimal performance and efficiency within the diverse Android environment.
  • Design and build robust, user-friendly developer-facing APIs, empowering the Android developer community to create innovative and engaging AI-powered user experiences.
  • Work closely with product teams to innovate and implement novel user experiences, leveraging techniques like Retrieval-Augmented Generation (RAG), traditional machine learning models, LoRAs, and targeted LLM fine-tuning.

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