Research Scientist Toronto
Research Scientist Toronto
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Job Description
Snapshot
The Google DeepMind Toronto team is dedicated to driving fundamental research breakthroughs across key areas of Artificial Intelligence. Our work spans multiple modalities (text image video) with core research efforts focused on advancing Large Language Models (LLMs) texttoimage and texttovideo generation intelligent agents and foundational representation learning. As a Research Scientist you will contribute to discovery by exploring novel algorithms developing new theories and pushing the frontiers of AI. We foster a highly collaborative supportive and dedicated environment where ambitious scientific exploration thrives.
About Us
Artificial Intelligence could be one of humanitys most useful inventions. At Google DeepMind were a team of scientists engineers machine learning experts and more working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery and collaborate with others on critical challenges ensuring safety and ethics are the highest priority.
The Role
Research Scientists at Google DeepMind lead our efforts in developing novel algorithmic and data solutions towards the end goal of solving and building Artificial General Intelligence.
As a Research Scientist on our team you will tackle fundamental challenges in enabling AI systems to learn remember and adapt over time mirroring capabilities essential for true intelligence. Your work will center on advancing memory and adaptation techniques specifically for multimodal large language models (MLLMs). We are exploring the frontiers of longterm and shortterm memory systems investigating how procedural memory can be effectively integrated and developing novel approaches to continual learning (including supervised reinforcement and incontext methods). A core focus will be mitigating catastrophic forgetting to ensure enduring knowledge and skill retention. Through this research we aim to create agents capable of persistent operation and reliable task in complex dynamic environments over long timescales a crucial step towards achieving AGI.
Key responsibilities:
- Lead the conceptualization design and implementation of novel algorithms models and architectures focused on memory systems (longterm shortterm procedural) within multimodal large language models (MLLMs) or other advanced AI systems.
- Develop investigate and push the boundaries of continual learning techniques (including supervised learning reinforcement learning incontext learning and potentially new paradigms) to enable robust adaptation over long timescales.
- Formulate and experimentally validate innovative strategies to explicitly address and mitigate catastrophic forgetting in learning systems.
- Design implement and refine rigorous evaluation methodologies metrics and benchmarks specifically tailored to assess memory efficacy adaptation speed knowledge retention and longterm task performance in complex dynamic environments.
- Plan execute and analyze largescale experiments to test fundamental hypotheses identify limitations of current approaches and demonstrate breakthroughs in building persistent and adaptive AI agents.
- Drive foundational research directions and contribute significantly to the scientific understanding of memory adaptation and lifelong learning as components of Artificial General Intelligence.
- Collaborate closely with other researchers and engineers across Google DeepMind sharing insights leveraging diverse expertise and contributing to the collective mission.
- Publish impactful research findings in leading peerreviewed AI conferences and journals advancing the stateoftheart in the field.
About You
In order to set you up for success as a Research Scientist at Google DeepMind we look for the following skills and experience:
- PhD in Computer Science a related technical field or equivalent practical experience demonstrating research expertise.
- Experience in core Machine Learning algorithms deep learning and research particularly in areas relevant to large language models (LLMs) sequence modeling or multimodal learning.
- Proficiency in Python and experience with major deep learning frameworks (e.g. PyTorch JAX TensorFlow).
- A track record of publications in toptier Machine Learning / AI conferences or journals (e.g. NeurIPS ICML ICLR EMNLP CVPR AAAI etc. demonstrating impactful contributions.
- Strong software engineering abilities with proven experience in designing and executing complex experiments (handson research).
In addition the following would be an advantage:
- Demonstrated research experience and publications specifically in one or more of the following areas:
- Memory longcontext processing and information retrieval (e.g. RAG) in large models.
- Model adaptation and lifelong learning (e.g. finetuning ICL continual learning RLbased adaptation).
- Multimodal representation learning and generation (text image video).
- Experience designing and implementing novel evaluation methodologies for AI capabilities.
- Experience with largescale model training and distributed computing environments.
- Experience working effectively in a collaborative research environment (industry or academic lab).
- Experience initiating leading or significantly contributing to ambitious research projects.
Applications close: Thursday 1st May.
At Google DeepMind we value diversity of experience knowledge backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex race religion or belief ethnic or national origin disability age citizenship marital domestic or civil partnership status sexual orientation gender identity pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation please do not hesitate to let us know.
Employment Type
Full Time
