Researcher Embodied AI Specialist

2 months ago

Montreal, Quebec, Canada Huawei Technologies Canada Co., Ltd. Full time
About the Role

We are seeking a highly skilled Researcher to join our Embodied AI team at Huawei Technologies Canada Co., Ltd. as a key contributor to our research and development efforts.

Key Responsibilities
  • Collaborate with our team of experienced researchers to tackle complex challenges in Embodied AI, driving innovation and advancing the state-of-the-art in the field.
  • Develop and implement cutting-edge approaches for Embodied AI applications, focusing on areas such as generative AI, representation learning, foundation models, reasoning, planning, data generation and augmentation, reinforcement learning, and low-level control.
  • Translate mathematical problem definitions and model/solution specifications into efficient executable code, ensuring seamless integration with our robotic platforms.
  • Conduct thorough evaluation and empirical studies using robotic platforms in both simulation and real-world environments, providing valuable insights for our research and development efforts.
  • Propose high-impact intellectual properties, such as patents, and publish or contribute to research papers in top-tier AI venues, further establishing Huawei's reputation as a leader in the field.
Requirements
  • M.Sc./Ph.D. degree in Computer Science or related fields (Exceptional candidates with a Bachelor's degree will be considered).
  • Prior experience in sequence analysis and generative AI, particularly in vision and language.
  • A proven research record in AI, demonstrated by having at least one paper as the first author in top-tier venues, such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA.
  • Proficiency in Python programming language.
  • Experience with one or more mainstream deep learning frameworks, e.g., PyTorch, TensorFlow.
  • Experience in robotics applications, particularly decision-making, planning, and control, or experience with real robots and ROS is an asset.
  • Experience with using large-scale datasets and sensor data, or with the use of various transformer architectures and diffusion models is an asset.
  • Experience with imitation and reinforcement learning algorithms is an asset.

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