Research Scientist

Found in: Jooble CA O C2 - 1 week ago

Markham ON, Canada Huawei Technologies Canada Co., Ltd. Full time

Our team has an immediate permanent opening for a Research Scientist.

Responsibilities:

  • Develop cutting-edge reinforcement learning and deep learning algorithms for behavioural planning, motion planning, and motion control.
  • Guide the development of solutions from prototyping to production and integration, including training on simulated environments or large-scale datasets, deploying on real-time robotic platforms, and testing and validating on self-driving vehicles.
  • Research on reinforcement learning, literature review, and publication.
  • Design, build and maintain large-scale production machine learning pipeline.
  • Work closely with experienced researchers to participate in the design, implementation and deployment of state-of-the-art perception algorithms and processing modules.
  • Iterative algorithm training and performance evaluation using publicly available datasets, Huawei’s own labelled datasets, and live on an autonomous vehicle.
  • Building perception software systems for autonomous driving. Such systems include 3D object detection, semantic/panoptic segmentation, classification, regression, prediction and localization, as well as 3D scene understanding, and sensor fusion and tracking, using both deep learning and classical machine learning techniques and multiple sensors such as LiDAR and camera sensors.

What you'll bring to the team:

  • Experience in relevant areas, publication records at top venues like NIPS/NeurIPS, ICML, ICLR, RLDM, AAMAS, AAAI or similar.
  • At least 10 years of in-depth hands-on programming experience, and proficient in Python, C/C++, TensorFlow, PyTorch, or MXNet, etc.
  • Proven experienceof translating ideas into research prototypes quickly.
  • Autonomous driving R&D experience. Leadership or management experience with a R&D team for proof of concept deliveries.
  • Strong grasp of fundamentals likelinear algebra, discrete and continuous optimization, supervised and unsupervised methods, and generative and discriminative methods.

#LI-BM1

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