Research Scientist, AI Secure Code, DeepMind

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world. As a Research Scientist in the CodeMender Research team, you will play a key role in both advancing our research capabilities and building the scalable infrastructure needed to support them. You will work on both the underlying cybersecurity models and custom orchestration harnesses—such as the skills and custom agent loops used for vulnerability verification. You will develop novel agentic techniques, integrate them into research prototypes, and productionize these solutions to land transformative impact for GDM and software security. Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority. We are pushing the boundaries across multiple domains. Our global teams offer learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . Contribute to GDM's Cyber Strike initiative, collaborating across organizations (DeepMind, Cloud Security, etc.) to build high-quality cybersecurity datasets, benchmarks, and evaluation platforms for vulnerability finding and fixing. Design, implement, and maintain scalable software infrastructure for CodeMender’s vulnerability detection and remediation services. Build evaluation pipelines, benchmarks, and leaderboards to rigorously measure agent performance. Optimize system performance, latency, and resource utilization for serving large models. Collaborate with product and platform teams to gather requirements and deploy research-based solutions into Google's developer tools and workflows. Minimum qualifications: PhD degree in Computer Science or Computer Engineering, similar technical field of study (e.g., Electrical Engineering, Mathematics, Information Technology) or equivalent practical experience. Experience with vulnerability research (e.g., auditing, reverse engineering, exploitation). Experience building infrastructure, developer tools, or ML-based applications. Publication record in AI conferences (e.g., NeurIPS, CVPR, ICCV, ICLR). Preferred qualifications: Bachelor's or Master's in Computer Science or equivalent experience. Experience building AI tools/agents for coding. Strong proficiency in Python, C++, or Go. Familiarity with machine learning frameworks and techniques (fine-tuning, reinforcement learning). Ability to self-initiate and a passion for the mission of AI safety and security. PhD degree in Computer Science or Computer Engineering, similar technical field of study (e.g., Electrical Engineering, Mathematics, Information Technology) or equivalent practical experience. Experience with vulnerability research (e.g., auditing, reverse engineering, exploitation). Experience building infrastructure, developer tools, or ML-based applications. Publication record in AI conferences (e.g., NeurIPS, CVPR, ICCV, ICLR).