Who are we?
Our team is the first in the world to use autonomous vehicles on public roads using end-to-end deep learning. With our multi-national world-class technical team, we’re building things differently.
We don’t think it’s scalable to tell an algorithm how to drive through hand-coded rules and expensive HD maps. Instead, we believe that machine learning algorithms learning from experience and data will allow our driver to be more intelligent and capable of easily adapting to new environments.
Our aim is to be the future of self-driving cars: the first to deploy in 100 cities across the world bringing autonomy to everyone, everywhere.
The impact you will have
Wayve’s approach to autonomous vehicles presents unique performance challenges. Our mission requires us to deploy state of the art modes that not only drive safely, but run in real time with strict limits on power consumption and heat produced. As advances in ML tend towards bigger with billions of parameters we have a significant challenge fitting these models on a car!
As a GPU performance engineer, you will be focussed on getting the most out of the hardware on our vehicles, and enabling us to deploy larger and more powerful models on the road. You will work directly with our model development engineers, to profile existing models and develop new architectures and optimisations designed to get the maximum performance possible from our hardware. You will also work with the robot hardware teams on design choices that maximise the model capacity and performance of our vehicles.
Challenges you will own
- Profiling, debugging, and optimising model architecture for fast inference
- Rewriting core parts of the model architecture for maximum performance
- Building a runtime environment that maximises the model capacity and speed
- Applying model optimisation techniques to improve training time
- Staying current with developments in GPU computing and machine learning techniques
- Deep knowledge of GPU architectures and how to squeeze the most out them
- Ability to identify, debug and fix performance bottlenecks in our models
- Experience with PyTorch and CUDA
- Strong Python ability
- Knowledge of model optimization techniques such as pruning and quantisation
- Experience with GPU profiling and debugging tools such as NVIDIA Nsight and Intel VTune
- Knowledge of more recent technologies such as Torch inductor or Triton
- A strong foundation in deep learning concepts would be helpful
- C++ experience
- A position to shape the future of autonomous driving, and thus to tackle one of the biggest challenges of our time
- Immersion in a team of world-class researchers, engineers and entrepreneurs
- Competitive compensation and stock options
- On-site chef and bar, lots of fun socials, a workplace nursery scheme and more!
- Help relocating/travelling to London, with visa sponsorship
- Flexible working hours - we trust you to do your job well, at times that suit you and your team.
Wayve is built by people from all walks of life. We believe that it is our differences that make us stronger, and our unique perspectives and backgrounds that allow us to build something different. We are proud to be an equal opportunities workplace, where we don’t just embrace diversity but nurture it - so that we all thrive and grow.
We asked employees of Wayve how satisfied they were with flexible working, and this is what they told us
Working at Wayve
London, UK and Mountain View, California