Tech Lead, Inference Performance, Onboard

The role 

As the Technical Lead for the Performance Optimisation Team at Wayve, you will play a key role in equipping the company with the tools and strategies needed to ensure its onboard software operates efficiently and reliably. You will lead the development of advanced profiling tools and performance analysis techniques, enabling a deep understanding of the software’s computational requirements. You will also use these tools to ensure that critical systems, such as AI inferencing, meet stringent timing and performance constraints. By architecting scalable and robust solutions, you will empower the team to optimise performance at both the software and hardware levels, providing a strong foundation to enable innovation in our autonomous driving technology.

In this role, you will also focus on building a foundation of knowledge and capability for the team by mentoring junior engineers and fostering a culture of technical excellence. You will work closely with the team’s manager to shape and deliver a roadmap that prioritises impactful performance analysis and optimisation initiatives. Beyond providing technical guidance, you will establish best practices in profiling and performance engineering, ensuring the team is equipped to address current and future challenges. This is your chance to lead the development of critical tools and techniques that will drive Wayve’s onboard systems to new levels of performance and reliability.

Key responsibilities: 

• Lead the team in developing and optimising software solutions to enhance overall system performance, ensuring efficient data transfer and low-latency processing across diverse hardware platforms.
• Define and oversee the design of robust, scalable systems that leverage advanced techniques in system and memory management to maximise real-time processing efficiency and minimise latency.
• Drive the identification and implementation of solutions to improve system performance, focusing on end-to-end data flow, resource utilisation, and real-time responsiveness.
• Provide expertise in profiling and debugging system performance using advanced tools and methodologies, guiding the team to identify bottlenecks and implement effective solutions.
• Act as a key liaison between software, hardware, and machine learning teams to ensure seamless data management, preprocessing, and optimal system utilisation, fostering an integrated approach to performance optimisation.
• Establish and champion industry best practices for high-performance, low-latency systems, setting a strong technical standard for the team and the broader organisation.
• Support hiring efforts and mentor team members in advanced techniques for system performance analysis and optimisation, fostering skill development and encouraging innovation in tackling complex performance challenges.

About you

Essential:

• Extensive experience in profiling, analysing, and optimising the performance of complex, real-time software systems.
• Strong background in developing software for embedded systems using high-performance system languages such as C++ and/or Rust.
• Deep understanding of system architecture, low-level programming, memory management, and resource utilisation, particularly for high-performance, real-time applications.
• Proven ability to lead and mentor engineers while working effectively across disciplines with hardware, software, and machine learning teams to deliver integrated solutions.
• Ability to articulate technical concepts clearly, foster collaboration, and tackle complex challenges with a hands-on, solution-oriented approach.
• Bachelor’s degree in Computer Science, Electrical Engineering, or a related field, or equivalent professional experience.

Desirable:

• Prior experience developing and deploying autonomous vehicle software on commercial automobiles, and/or knowledge of ASPICE, DriveOS, or AutoSAR.
• Proven experience in GPU programming and optimization, with proficiency in CUDA, OpenCL, or other GPU programming frameworks.
• Experience with QNX or similar real-time operating systems.
• A Master’s degree or greater in Computer Science, Electrical Engineering, or a related field.