About the Company

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

In our fast-paced environment, big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.

At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.

About the Role

Job Title: Engineering Manager, GPU Kernel
Location: London, England, United Kingdom
Employment Type: Full-time
Department: Engineering
Workplace Type: Hybrid

Key Responsibilities

• Lead a multi-disciplinary team of ML GPU kernel engineers to enable efficient ML deployments across millions of customer vehicles.
• Set key foundational strategy on deployment frameworks, compilers, toolchains, and SoCs in collaboration with tech leads.
• Set clear objectives and priorities, and allocate resources efficiently.
• Develop new skills, especially within end-to-end ML and inference optimization.

About You

Essential Skills and Experience

• Proven experience as an Engineering Manager delivering complex engineering projects.
• Experience developing GPU kernels and/or ML compilers (e.g., CUDA, OpenCL, TensorRT, MLIR, TVM, etc.).
• Experience optimizing systems to meet strict utilization and latency requirements.
• Excellent interpersonal and communication skills.

Desirable Skills and Experience

• Experience with C++ and ML frameworks such as PyTorch.
• Experience with ML deployment pipelines.
• Experience with embedded SoCs used in automotive environments (e.g., Nvidia, Qualcomm, Renesas, etc.).

We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.