{"id":3677677,"url":"https://alion.io/job/google-deepmind-research-scientist-memory-reasoning-and-continual-learning","title":"Research Scientist, Memory, Reasoning and Continual Learning","company":{"id":4,"name":"Google DeepMind","domain":"deepmind.google","url":"https://alion.io/company/deepmind","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-11-03T10:10:04Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-09T05:15:51Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"About the job\nAs 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.\nAs 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.\nOur 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.\nArtificial 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.\nWe 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.\nIndividual pay is determined by factors including job-related skills, experience, and relevant education or training.Canada: $185000 - $190000 (CAD) + 15% bonus target + equity + benefits\nLearn more about benefits at Google.\nResponsibilities\nInitiate 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.\nDesign 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.\nDevelop 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.\nBuild and improve infrastructure for high-capacity context windows, dynamic memory structures, and continuous training pipelines in close collaboration with development teams.\nCommunicate 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.\nQualifications\nMinimum qualifications:\nPhD in Computer Science, Machine Learning, Mathematics, Cognitive Science, or a related technical field, or equivalent practical experience.\n2 years of experience in artificial intelligence research, including publications in conferences or journals (e.g., NeurIPS, ICML, ICLR).\nExperience with Deep Learning, Reinforcement Learning, Natural Language Processing, or architectures for Continual/Lifelong Learning.\nExperience in Python and deep learning framework (e.g., JAX, TensorFlow, PyTorch).\nExperience in algorithms design, running experiments, and analyzing results.\nPreferred qualifications:\nPostdoctoral or equivalent industry research experience focusing on large language models, autonomous agents, or long-context memory systems.\nExperience designing novel benchmarks or evaluation frameworks for complex planning and reasoning capabilities.\nKnowledge of the interdisciplinary intersection between machine learning and neurobiology or computational neuroscience.\nAbility to contribute to open-source ML software or experience in collaborating with cross-functional teams.\nFamiliarity with distributed training techniques and building infrastructure for high-capacity context 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