Applied Scientist
Description For Applied Scientist
Wayve, founded in 2017, is at the forefront of Embodied AI technology development, focusing on creating advanced autonomous driving systems. Our mission is to develop AI software and foundation models that enable vehicles to navigate complex environments safely and efficiently, without relying on maps.
As an Applied Scientist, you'll be tackling some of the most challenging problems in autonomous driving, working with foundation models for robotics, reinforcement learning, and large language models. You'll be part of a diverse, cross-disciplinary team developing solutions across perception, prediction, planning, and control.
The role involves leveraging extensive real-world driving data, architecting models using cutting-edge advances in foundation models and transformers, implementing various learning algorithms, and scaling models efficiently. You'll have opportunities to contribute to top-tier academic conferences like NeurIPS, CVPR, and ICRA.
Wayve offers a unique environment where your contributions directly impact the future of autonomous driving. We value diversity, embrace new perspectives, and foster an inclusive work culture. Our team tackles big problems with humility and continuous learning, working towards creating autonomy that propels the world forward.
Join us in developing intelligent, mapless, and hardware-agnostic AI products designed for automakers, accelerating the transition from assisted to automated driving. At Wayve, you'll be part of a team that's not just developing technology, but shaping the future of transportation.
Responsibilities For Applied Scientist
- Work on foundation models for robotics
- Develop model-free and model-based reinforcement learning solutions
- Research and implement offline reinforcement learning
- Work with large language models
- Develop planning with learned models, model predictive control and tree search
- Research imitation learning, inverse reinforcement learning and causal inference
- Create learned agent models for cars, people, and other dynamic agents
- Leverage large real-world driving data sets
- Architect models using latest advances in foundation models and transformers
- Scale models efficiently across data, model size, and compute
- Contribute to academic publications for top-tier conferences
Requirements For Applied Scientist
- Thorough knowledge of and 7+ years applied experience in AI research, computer vision, deep learning, reinforcement learning or robotics
- Ability to deliver high quality code and familiarity with deep learning frameworks (Python and PyTorch preferred)
- Experience leading a research agenda aligned with larger goals
- Industrial and/or academic experience in deep learning, software engineering, automotive or robotics
- Experience working with training data, metrics, visualization tools, and in-depth analysis of results
- Ability to understand, author and critique cutting-edge research papers
- PhD in a relevant area and/or track records of delivering value through machine learning (big plus)
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