Machine Learning Engineer - Training Performance

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 seeking skilled engineers to join our Training Tech team working on optimising large scale training jobs as we aim to scale our models through the next order of magnitude. A successful candidate will increase efficiency of training jobs in order to allow Wayve to train larger models faster.

Key responsibilities:

• Profile training jobs to identify their bottlenecks, e.g. using NVIDIA Nsight Systems


• Design and implement efficiency improvements to maximise MFU, e.g. tensor parallelism, model compilation, mixed precision


• Design and implement observability tools, e.g. to track MFU


• 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 as a ML Engineer (Training Performance) at Wayve, we’re looking for the following skills and experience.

Essential

• Experience optimize large scale training jobs on GPU compute clusters.


• Experience in working in platform teams and working with research teams.


• Experience in reporting and tracking over time benchmarked performance 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

• Solid experience working with concurrent, parallel and distributed computing.


• Experience using Nvidia NSight Systems.


• Experience implementing GPU kernels.


• Knowledge of computing fundamentals - what makes code fast, secure and reliable.

This is a full-time role based in our office in Sunnyvale. 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 operate core working hours so you can determine the schedule that works best for you and your team.

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.