Software Engineer, AI Libraries

Job description

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’re looking for a Software Engineer to join our AI Libraries team. This team builds and maintains the platforms, libraries, and tools that enable Wayve’s ML engineers and researchers to train, evaluate, and scale models efficiently. This is a hands-on software engineering role focused on building stable, scalable, and modular systems that support large-scale ML development. You’ll work closely with ML teams across Wayve to understand their needs, design reusable abstractions, and improve the reliability, performance, and usability of our training infrastructure. You’ll play a key role in maturing Wayve’s AI platform and helping bring autonomous driving technology into the hands of customers. Key responsibilities Design, build, and maintain scalable Python libraries and tools used by ML engineers and researchers across Wayve. Develop robust abstractions for data loading, distributed training, inference, checkpointing, and model evaluation workflows. Support training at scale across large GPU clusters and cloud-based infrastructure. Work closely with ML teams to understand user needs and create tools that are reliable, well-documented, observable, and easy to adopt. Improve engineering quality across ML systems through strong software architecture, testing, monitoring, and maintainability practices. Optimise data and training pipelines to support multi-modal data sources, inc…