Staff ML Performance Engineer (Training Efficiency)

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!  

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

In order to set you up for success in this role, we’re looking for the following skills and experience.

• 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

• 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.

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