Staff ML Performance Engineer (Training Efficiency)

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 sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition  (including breastfeeding) or any other basis as protected by applicable law.   About us    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.   Make Wayve the experience that defines your career!   The role We are looking for a Staff ML Performance Engineer to join our Training Tech team working on optimizing large scale ML jobs to enable scaling our models to the next order of magnitude. A successful candidate will increase efficiency of training and inference workloads in order to allow Wayve to train larger models faster. Key responsibilities: Profile ML workloads to identify their bottlenecks, e.g. using NVIDIA Nsight Systems Design and implement efficiency improvements to maximize MFU and throughput, e.g. parallelism, model compilation, mixed precision Design and implement observability tools to identify bottlenecks and drive performance improvements, e.g. to track MFU, throughput, latency, etc Design and implement benchmarking tools, e.g. to track efficiency gains or regressions Collaborate closely with Research teams to integrate training efficiency improvements and create a culture of performance optimization About you In order to set you up for success in this role, we’re looking for the following skills and experience. Essential 10+ years of industry experience driving performance engineering across ML systems, GPU compute infrastructure, distributed platforms or similar field. Experience optimizing large scale jobs on GPU compute clusters. Experience in working in platform teams and working with research teams. Experience in writing, reporting, and tracking performance benchmarks in an open and accessible way. Ability to write high quality, well-structured and tested Python code BS or MS in Machine Learning, Computer Science, Engineering, or a related technical discipline or equivalent experience Desirable Experience working with concurrent, parallel and distributed computing. Experience using NVIDIA NSight Systems or other system profilers. Experience implementing GPU kernels (CUDA, Triton, etc). Knowledge of computing fundamentals - what makes code fast, secure and reliable. #LI-HH1 Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know 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. For more information visit Careers at Wayve.  To learn more about what drives us, visit Values at Wayve  DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.