Software Engineer II, Applied ML Engineer for AICore

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development in Python and C++, or 1 year of experience with an advanced degree.
  
• 2 years of experience with machine learning algorithms and tools (e.g. TensorFlow), artificial intelligence, deep learning or natural language processing.
  
• Experience in Machine Learning or Artificial Intelligence.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
  
• Knowledge of mobile app development on Android.
  
• Understanding of generative AI concepts (LLMs, diffusion models, etc.), development workflows, and use cases.
  

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.

This is your opportunity to become an integral part of an innovative and high-impact project that is transforming the Android ecosystem. We are seeking an enthusiastic applied ML engineer to help us harness the power of on-device General AI models, including Gemini Nano, to elevate the interaction and satisfaction level for Android users.

Our team is dedicated to crafting a state-of-the-art GenAI capability by fully harnessing the potential of LLMs and other foundational AI models running directly on mobile devices. Our mission is to innovate from within the Android platform and extend these benefits outward, influencing mobile experiences everywhere.

Android is Google’s open-source mobile operating system powering more than 3 billion devices worldwide. Android is about bringing computing to everyone in the world. We believe computing is a super power for good, enabling access to information, economic opportunity, productivity, connectivity between friends and family and more. We think everyone in the world should have access to the best computing has to offer. We provide the platform for original equipment manufacturers (OEMs) and developers to build compelling computing devices (smartphones, tablets, TVs, wearables, etc) that run the best apps/services for everyone in the world.

The US base salary range for this full-time position is $136,000-$200,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Collaborate closely with Google DeepMind, CoreML to adapt and implement the Gemini model for mobile user applications.
  
• Tailor and enhance GenAI models, specifically Gemini Nano, ensuring they are optimally integrated into the mobile environment.
  
• Engage in prototyping with product teams, aiming to pioneer novel user experiences by elevating mobile capabilities through methods such as Retrieval-Augmented Generation (RAG), traditional Machine Learning models, LoRAs or targeted LLM fine-tuning.
  
• Perform model evaluations, including benchmarking and performance analysis, to ensure the delivery of premier user experiences.