Staff Research Engineer, GenAI Reinforcement Learning, DeepMind
Listed on Alphabet’s official careers site. Listed as of October 30, 2026. Check that it is still open and apply there.
Apply on Alphabet’s careers site →Job description
Minimum qualifications
Bachelor’s degree or equivalent practical experience. 8 years of experience in software development. 5 years of experience testing, and launching software products. 3 years of experience with software design and architecture. Experience with reinforcement learning, machine learning, and deep learning.
Preferred qualifications
Master’s degree or PhD in Engineering, Computer Science, or a related technical field. 8 years of experience with data structures and algorithms. 3 years of experience in a technical leadership role leading project teams and setting technical direction. 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects. Experience in developing reinforcement learning algorithms, framework or infrastructure.
About the job As a Research Engineer/Scientist working on Reinforcement Learning (RL) in Gen AI, you will be on the critical path of further improving our foundational models into more helpful and powerful AI agents through reinforcement learning. This is a unique opportunity that has a direct and profound impact on the utility and safety of one the world's most advanced AI systems. 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.
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