Research Scientist PhD Intern, Summer 2027
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Minimum qualifications:
- Currently enrolled in a PhD program in Computer Science and Engineering, Machine Intelligence and Data Science, AI or a related technical field.
- Experience in one or more areas of Machine Learning (e.g., Large Language Models, Deep Learning, Natural Language, Computer Vision, etc.).
- Experience programming in Python, Java.
Preferred qualifications:
- Currently enrolled in a full-time degree program and returning to the program after completion of the internship.
- Experience as a researcher, including internships, full-time, or at a lab.
- Experience contributing to research communities or efforts, including publishing papers in conferences or journals.
- Experience with one or more of AI/ML frameworks.
- Experience with security-related research, systems, or technologies.
- Ability to design and execute on research agendas.
About the job
Research happens across Google everyday, in many different teams. Our research has already impacted user-facing services across Google including Search, Maps, and Google Now, and is central to the success of Google Cloud and our computing, storage, and networking infrastructure.
Research Interns work with Research Scientists and Software Engineers to discover, invent, and build at scale. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes. From creating experiments and prototyping implementations to designing architectures, Research Interns work on challenges in artificial intelligence, machine perception, data mining, machine learning, natural language understanding, privacy, computer architecture, networking, operating systems, storage and data management, and more. You are also expected to contribute to the wider research community by publishing papers.
Team Specifications:Google’s Core Organization builds the foundational systems, technical frameworks, and platforms that power Google products worldwide. Within Core, our overarching mission is simple yet profound: to make Google work better for everyone at scale. Within Core, the Privacy, Safety, and Security (PSS) organization is dedicated to keeping everyone safe at scale—protecting billions of users, safeguarding their data, and securing Alphabet and the broader internet ecosystem. We operate on the principle that products must be secure by default and private by design.
Our research teams bridge the gap between theoretical computer science and planetary-scale engineering. We bring together researchers and engineers across cryptography, adversarial machine learning, privacy-preserving computation, systems security, and trustworthy AI. As a Research Scientist in Core PSS, you will pursue foundational and applied scientific breakthroughs—investigating emergent threat vectors, developing next-generation privacy and safety primitives, and creating mathematical and empirical guarantees that redefine digital safety. You will collaborate closely with core engineering hubs to translate novel proofs-of-concept into production deployments safeguarding billions of global users, while maintaining active contributions to the scientific community through first-tier publications, open-source initiatives, and academic partnershipsGoogle is and always will be an engineering company. We hire people with a broad set of technical skills who are ready to address some of technology's greatest challenges and make an impact on millions, if not billions, of users. At Google, engineers not only revolutionize search, they routinely work on massive 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
- Participate in research to develop solutions for problems.
- Research, conceive, and develop AI/ML solutions to accelerate.
- Contribute to deeper understanding of limitations of AI/ML architectures, trade-offs and optimizations.
- Lay down the next steps for adoption of the research into next generation product development.
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