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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. The company is pioneering a mapless, hardware-agnostic approach that enables vehicles to learn, adapt, and improve at scale.
You'll join the AV Core organisation's Core Model Safety team, which focuses on collision avoidance, model understanding, and robustness under failure. This is a senior individual contributor role on a focused, high-impact team working with large-scale training and fleet data.
Your responsibilities will include:
- Leading large-scale data enrichment and curation initiatives across pre-training, mid-training, reward modelling, self-supervised labelling, and the Data Enrichment Platform
- Building workflows for mining hard and rare events from real-world data
- Investigating how models perform in production and identifying failure modes
- Designing and training out-of-distribution detection mechanisms for robust autonomous operation
- Working at the intersection of model engineering, infrastructure, compute, and data to shape architecture across teams
- Improving overall efficiency of the machine learning and operations stack
You'll collaborate across research and engineering boundaries, bringing technical expertise and clear communication to guide substantial technical areas.
Wayve operates hybrid working with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company offers relocation support and visa sponsorship where applicable, learning and development budgets, comprehensive benefits including health insurance, dental, enhanced parental leave, retirement/pension, and wellbeing partnerships.
REQUIREMENTS:
Essential:
- Hands-on experience with ML systems deployed in production environments
- Proficiency in Python and PyTorch with strong software engineering practices
- Proven experience building reliable machine learning training and evaluation systems
- Strong track record with ML operations and infrastructure, including distributed computing and large-scale inference
- Senior-level ownership and collaboration skills; ability to lead substantial technical areas, work across research/engineering boundaries, and communicate clearly
Desirable:
- Prior experience in autonomous vehicles or robotics with hands-on deployment and closed-loop validation on physical systems
- Experience mining, generating, or evaluating rare events using simulation and fleet or real-world data
- Experience setting up cloud-based monitoring solutions for large-scale operations (dashboards, logging, real-time alerts)
- Experience with transformer-based and multimodal architectures, including vision-language models (VLM), vision-language-action models (VLA), or equivalent
- Proficiency in C++, CUDA, distributed training, or performance optimization for production ML systems
