Applied Scientist
2 days ago
🏢 In-office - Vancouver
Description
• Established in 2017, Wayve is a leader in autonomous vehicle technology, driven by breakthroughs in Embodied AI. • Our intelligent, mapless, and hardware-agnostic technologies empower vehicles to navigate complex environments effortlessly. • We are seeking an experienced researcher to be a founding member of our Vancouver team! • You'll be a key member of our diverse, cross-disciplinary team, helping teach our robots how to drive safely and comfortably in complex real-world environments. • This encompasses many aspects of research across perception, prediction, planning, and control.
Requirements
• Proven track record of research in one or more of the topics above demonstrated through deployed applications or publications. • Strong programming skills in Python, with experience in deep learning frameworks such as PyTorch, numpy, pandas, etc. • Experience bringing a machine learning research concept through the full ML development cycle • Excellent problem-solving skills and the ability to work independently as well as in a team environment. • Demonstrated ability to work collaboratively in a fast-paced, innovative, interdisciplinary team environment. • Experience bringing an ML research concept through to production and at scale • PhD in Computer Science, Computer Engineering, or a related field
Benefits
• The chance to be part of a truly mission driven organisation and an opportunity to shape the future of autonomous driving. • Competitive compensation and benefits • A dynamic and fast-paced work environment in which you will grow every day - learning on the job, from the brightest minds in our space, and with support for more formal learning opportunities too • A culture that is ego-free, respectful and welcoming • A hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We also operate core working hours so you can be where you need to be for family and loved ones too. Teams determine the routines that work best for them.
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