Where you will have an impactAs an Embedded Kernel Engineer within our dynamic team, you'll be instrumental in deploying Wayve's autonomous vehicle (AV) AI model across consumer vehicles. Your role is crucial in developing model compilers and crafting high-performance kernels for efficient inferencing on embedded GPU environments. Through close collaboration with machine learning engineers, you'll pinpoint opportunities to amplify inference performance by optimally utilizing the hardware capabilities of various deployment platforms. Your deep understanding of GPU architecture, from memory management to the intricacies of GPU cores, will be pivotal. Utilizing tools like TensorRT and model compilers, you will push the boundaries of what's possible in inference performance in the embedded environment.Challenges you will ownOptimization Leadership: Lead efforts to discover the optimal model compilation strategies that harmonize compute intensity, caching, and memory bandwidth to maximize hardware utilization on targeted platformsPrecision Transformation: Innovate in transforming large AI models to low precision implementations, ensuring minimal accuracy lossGPU Architecture Mastery: Become the go-to authority on GPU architecture for targeted hardware platforms, such as NVIDIA Orin or Qualcomm SnapdragonModel Compilation Process Creation: Design and implement the process to convert AI models from a PyTorch framework to native platform-specific programs, enhancing model efficiency and performanceWhat you will bring to WayveGPU Development Expertise: A minimum of 5 years of direct experience in developing kernels for GPUs, showcasing an ability to solve complex computational challenges.Advanced C++ Skills: Proficiency in C++ programming, with a demonstrated history of developing efficient, high-quality code.GPU Programming Tools Proficiency: Extensive experience with GPU programming using tools like CUDA and TensorRT,



specifically within embedded environments.Deep GPU Design Knowledge: A thorough understanding of GPU design and operations, including familiarity with AI accelerators.Quantization and Compiler Experience: Expertise in model quantization, particularly in implementing low precision formats, and experience with developing and using model compilers.Educational Qualifications: A Master's degree in a relevant field, supplemented by research experience.What we offer youThe chance to be part of a truly mission driven organisation and an chance to shape the future of autonomous driving. Unlike our competitors, Wayve is still relatively small and nimble, giving you the chance to make a huge impactCompetitive compensation and benefitsA 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 tooA culture that is ego-free, respectful and welcoming (of you and your dog) - we even eat lunch together every dayBenefits such as an onsite chef, workplace nursery scheme, private health insurance, cycle scheme, therapy, yoga, two onsite bars, large social budgetsThis is a full-time role based in our office in London. At Wayve we want the best of all worlds so we operate 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.At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of gender, gender identity, gender expression, race, sexual orientation, physical or mental disability, ethnicity, age or religious belief #J-18808-Ljbffr

📌 Gpu Kernel Engineer, Onboard Software Platform (Vancouver)
🏢 Wayve
📍 Vancouver