Minimum qualifications:
- PhD degree in Computer Science, ML/AI, or a related field, or equivalent practical experience.
- Experience coding in one of the following programming languages including but not limited to: Python, C, C++, Java, JavaScript or Golang.
- Experience in Machine Learning or Artificial Intelligence.
Preferred qualifications:
- Research experience in designing, developing, or applying ML/AI systems or applications in a large-scale distributed environment.
- Experience in designing, training, or refining complex ML/AI models.
- Experience in deep learning frameworks like TensorFlow/Jax/Pytorch.
- Experience in building a stack for an AI-powered application, including data ingestion and processing pipelines, building APIs, and connecting the model to a user-facing interface.
- Familiarity with model architectures (CNNs, NLP Transformers, Diffusion/Vision Transformers).
- Availability to start full-time role in 2026.
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 engineers develop 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 a massive scale. You'll be at the forefront of innovation, developing systems and AI and Machine Learning solutions.
As a PhD graduate, your research expertise is invaluable to us. Explore a variety of projects, collaborate with various teams, and contribute to products that are changing the world, across many product areas, including AI & Infrastructure, Cloud, YouTube, Search, Ads and more!
Our engineering teams include thousands of PhDs who bring their deep knowledge and research experience to enhance our systems and products. As a Google PhD Software Engineer, you will work on critical projects, with many opportunities to learn and follow your interests. We expect our engineers to be creative and versatile, leading and identifying new problems to push the field and Google technology forward.
Google offers you exciting opportunities as it is one of the world’s leading producers and consumers of ML and AI technology, with decades of experience in designing, deploying, and using ML software and custom ML hardware infrastructure at massive scale.
The US base salary range for this full-time position is $141,000-$202,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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.Responsibilities
- Collaborate or lead on team projects to carry out design, analysis, and development of advanced ML systems across the stack using your research expertise.
- Support building end-to-end ML Systems that involves working across the full stack, from low-level hardware acceleration and compiler optimizations to high-level model architecture and production APIs, transforming your research expertise into robust, scalable products.
- Optimize complex system performance by analyzing and fixing performance bottlenecks, memory inefficiencies, and errors in production systems to meet stringent customer goals.
- Elevate engineering excellence by writing well-tested code, conducting code reviews and fostering a culture of quality by advocating best engineering practices.
