Research Engineer, DeepMind

Design and build the self-improving agent framework — the scaffolding, diagnostic tools, and feedback loops that enable continuous quality improvement across Science, Cybersecurity, and Coding agents.Engage directly with enterprise customers, public sector teams, and academic partners to deploy, monitor, and improve agents in real-world settings.Build automated evaluation pipelines that capture real-world agent quality beyond lab benchmarks.Develop cross-engagement learning systems that generalize insights from individual deployments to improve all agents at scale.Identify and characterize core model limitations with empirical evidence, partnering with GDM research teams to drive foundational improvements while guiding the transition from direct customer engagements to a scalable, self-improving deployment model.Minimum qualifications:Bachelor's degree in Computer Science, a related Software Engineering field, or equivalent practical experience.8 years of experience in software development with Python.Experience building and developing large-scale infrastructure or distributed systems.Preferred qualifications:Experience in life sciences, cybersecurity, materials, or AI within education and healthcare.Experience with Generative AI, large language models (LLMs), or conversational AI systems, especially in the development of AI agents or agentic frameworks.Proficiency with MapReduce, Hadoop, Spark, Flume, Hive, Impala, SparkSQL, BigQuery.At Google DeepMind our mission is to build the world's first general-purpose learning agent. Central to this mission is the complex task of measuring the intelligence of our prototypes. As a Research Engineer, you will be working with the cutting edge AI agents developed by our exceptional team of Machine Learning and Neuroscience research scientists. Your responsibilities will include everything from creating systems for agent testing using 2D and 3D games to developing test problems within physics simulators. You will create graphical visualization of results, build competitive agent leaderboards and test new algorithms on robots. To succeed in this role you will need to have a strong foundation in software engineering and enjoy working on a wide range of challenging problems within a mission-driven team.Google DeepMind's Science and Strategic Initiatives (SSI) unit is building a team focused on commercialization and real-world academic impact of AI models and agents across three domains: Science, Cybersecurity, and Coding Agents.To achieve impact at scale, we are building a self-improving agent framework — a system that can automatically diagnose failure modes, generalize learnings across customer engagements, and continuously improve agent quality at scale. You will be at the foundation of this effort, working directly with enterprise customers and academic researchers to understand product opportunities directly, while simultaneously building the infrastructure to optimize and improve our products quickly and accurately.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 diverse 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: $262000 - $365000 (USD) + 25% bonus target + equity + benefitsLearn more about benefits at Google.Bachelor's degree in Computer Science, a related Software Engineering field, or equivalent practical experience.8 years of experience in software development with Python.Experience building and developing large-scale infrastructure or distributed systems.