Reinforcement Learning Researcher - Autonomous Driving

Company Description

About Huawei

Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. With integrated solutions across four key domains – telecom networks, IT, smart devices, and cloud services – we are committed to bringing digital to every person, home and organization for a fully connected, intelligent world.

At Huawei, innovation focuses on customer needs. We invest heavily in basic research, concentrating on technological breakthroughs that drive the world forward. We have more than 180,000 employees, and we operate in more than 170 countries and regions. Founded in 1987, Huawei is a private company fully owned by its employees.

About Huawei Canada

Huawei Canada helps connect Canadians to world-leading high-speed wireless Internet – and supplies them with cutting-edge smart devices. In partnership with Canadian telecommunications providers, we work to bring the benefits of a reliable and secure digital experience to every person, home and organization, including those in rural and remote areas of the country. Huawei first came to Canada in 2008. Today, the company employs more than 1,100 Canadians in research and development, IT, sales and other fields. Huawei Canada is an active supporter of many charitable and community initiatives from coast to coast.

Job Description

1. Develop cutting edge reinforcement learning and deep learning algorithms for behavioral planning, motion planning, and motion control.
2. 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.
3. Research on reinforcement learning, literature review, and publication.
4. Design, build and maintain large scale production machine learning pipelines.

Qualifications

• PhD in Robotics, Machine Learning, Computer Science, or related fields.
• Proven track record of experience in relevant areas (significant industry experience in Reinforcement Learning and publication record at top venues like NIPS/NeurIPS, CoRL, ICML, ICLR, RLDM, AAMAS, AAAI or similar.
• Strong grasp of fundamentals: linear algebra, discrete and continuous optimization, supervised and unsupervised methods, generative and discriminative methods.
• Expertise in one or more focus areas: model-free and model-based reinforcement learning, imitation learning, inverse reinforcement learning, safe RL, multi-agent training, Bayesian inference, etc.
• In-depth hands-on at least 2 yrs working experience in Python, C/C++, TensorFlow, PyTorch, MXNet, Keras, etc. and a track record of translating ideas into research prototypes quickl

Additional Information

Preferred:

• Passion for robotics, machine learning, and control systems.
• Experience in graph networks, adversarial training, domain randomization, intention-aware planning, and planning under uncertainty.
• Experience applying reinforcement learning to robotic and hardware-in-the-loop systems.
• Prior work experience in self-driving or automotive applications.