About the Role As an LLM Agent Research Scientist, you'll define and lead research that pushes the limits of what LLM-powered agents can do. You'll design agent architectures, reasoning and planning methods, tool-use and multi-agent frameworks, and the post-training recipes (including RL) that make agents capable and dependable. You'll also train models with high-end GPUs on large, high-quality datasets. You'll sit at the intersection of frontier research and product, directly shaping how LLM agents transform the way people create and get work done in Canva. 作为大模型智能体研究科学家，你将定义并主导不断突破智能体能力上限的研究。你将设计智能体架构、推理与规划方法、工具调用与多智能体框架，以及让智能体更强大、更可靠的后训练方案（包括 RL）。你也会在大规模、高质量的数据集上，用高端 GPU 训练模型。你将处在前沿研究与产品的交汇点，直接影响大模型智能体如何改变人们在 Canva 上创作与工作的方式。 At the moment, this role is focused on • Research Direction: Determining and leading agent research initiatives aligned with the frontier of LLM/agent research and Canva's product goals. • Model Innovation: Creating novel agent capabilities through post-training and RL—developing reward modeling, synthetic data, and environment design—and running experiments with high-end GPUs on large, high-quality datasets to validate hypotheses and translate results into shippable improvements. • Harness Innovation: Designing and building the agent harness that lets agents execute complex, long-horizon tasks reliably in production, and exploring harness techniques for self-improvement. • Evaluation: Creating rigorous benchmarks and evaluation methods for agent correctness, safety, robustness, and real-world task success. • Product Impact: Partnering closely with engineering, product, design, and data teams to land frontier research capabilities in real Canva product experiences. • 研究方向：定义并主导契合大模型/智能体研究前沿与 Canva 产品目标的研究方向。 • 模型创新：通过后训练和 RL 打造全新的智能体能力——涵盖 reward modeling、合成数据、环境设计等——并在大规模、高质量数据集上用高端 GPU 开展实验，验证假设，把结果转化为可落地的改进。 • Harness 创新：设计并构建 agent harness，让智能体能在生产环境中稳定地完成复杂的长链路任务，并探索让智能体自我改进的 harness 技术。 • 评测：围绕智能体的正确性、安全性、鲁棒性和真实任务完成情况，构建严谨的基准与评测方法。 • 产品影响：与工程、产品、设计和数据团队紧密协作，将前沿研究能力落地到真实的 Canva 产品体验中。 You're probabaly a match if you have: • Rich, hands-on experience developing and iterating on LLM-based agents or foundation models—covering areas such as post-training, RL, reward modeling, synthetic data, or agent environment design. • A strong academic and professional track record, including peer-reviewed publications at top-tier venues such as NeurIPS/ICML/ICLR/ACL/EMNLP/CVPR and/or impactful open-source contributions. • Proficiency in Python and PyTorch, with familiarity in frameworks and libraries such as Transformers, DeepSpeed, Megatron, and agent/RL frameworks, as well as cloud computing platforms for efficient training and deployment. • Curiosity! Always looking to stay ahead of industry trends by tracking AI literature, competitors, and emerging technologies. • 在研发和迭代大模型智能体或基础模型方面有丰富的实战经验——涵盖后训练、RL、reward modeling、合成数据或智能体环境设计等方向。 • 扎实的学术与专业履历，例如在 NeurIPS/ICML/ICLR/ACL/EMNLP/CVPR 等顶级会议发表过学术论文，或有过有影响力的开源贡献。 • 精通 Python 和 PyTorch，熟悉 Transformers、DeepSpeed、Megatron 及 agent/RL 框架等工具库，以及用于高效训练和部署的云计算平台。 • 好奇心！始终关注行业动态，通过追踪 AI 文献、竞品和新兴技术保持领先。 该岗位现面向所有经验阶段的候选人开放，包括社会招聘、应届毕业生，同时开放实习生岗位。工作地点为北京。欢迎申请，期待你的加入！ Notice: This position is open to candidates at all experience levels, including experienced candidates, graduates, as well as internship opportunities. The role is based in Beijing. We welcome your application and look forward to having you on board!