Research Scientist, Memory, Reasoning and Continual Learning
Confirmed on the employer's own hiring board on Nov 7, 2026. First seen by Alion on Nov 3, 2026.
About the job
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.
Our Research Scientists at DeepMind are at the forefront of advancing artificial intelligence. In this role, you will join our team focused on pushing forward fundamental research and technology in Artificial Intelligence, specifically in the domains of Memory, Reasoning, and Continual Learning. This role offers the opportunity to contribute to groundbreaking research and publish in venues.
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.Canada: $185000 - $190000 (CAD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Initiate and lead novel research directions, applying insights from machine learning and related fields (e.g., NLP, Reinforcement Learning (RL), computational neuroscience) to advance long-context attention mechanisms, Retrieval-Augmented Generation (RAG), continual learning architectures, and multi-step reasoning frameworks.
- Design and execute end-to-end experiments, proposing and testing hypotheses to better align model-based agents, mitigate catastrophic forgetting, and improve sample efficiency in non-stationary environments.
- Develop evaluations and benchmarks that stress-test long-horizon memory, out-of-distribution generalization, and complex planning capabilities, including in-depth search debugging of failure modes.
- Build and improve infrastructure for high-capacity context windows, dynamic memory structures, and continuous training pipelines in close collaboration with development teams.
- Communicate research findings clearly through plots, writeups, and paper-ready narratives, while contributing to a team culture of first-principles thinking, high standards, and constructive feedback.
Qualifications
Minimum qualifications:
- PhD in Computer Science, Machine Learning, Mathematics, Cognitive Science, or a related technical field, or equivalent practical experience.
- 2 years of experience in artificial intelligence research, including publications in conferences or journals (e.g., NeurIPS, ICML, ICLR).
- Experience with Deep Learning, Reinforcement Learning, Natural Language Processing, or architectures for Continual/Lifelong Learning.
- Experience in Python and deep learning framework (e.g., JAX, TensorFlow, PyTorch).
- Experience in algorithms design, running experiments, and analyzing results.
Preferred qualifications:
- Postdoctoral or equivalent industry research experience focusing on large language models, autonomous agents, or long-context memory systems.
- Experience designing novel benchmarks or evaluation frameworks for complex planning and reasoning capabilities.
- Knowledge of the interdisciplinary intersection between machine learning and neurobiology or computational neuroscience.
- Ability to contribute to open-source ML software or experience in collaborating with cross-functional teams.
- Familiarity with distributed training techniques and building infrastructure for high-capacity context windows.