wayve - Senior Machine Learning Engineer, AV Core

• You’ll lead large-scale data enrichment and curation initiatives, collaborating across pre- and mid-training, reward modelling, self-supervised labelling, and the Data Enrichment Platform. You’ll develop workflows for mining hard and rare events, investigate how models perform in the real world, and improve the efficiency and reliability of our machine learning operations stack. • 🧩 What you’ll be working on • Lead on large-scale data enrichment within the Core Model Safety team, including collaborations with Pre/Midtraining, reward modelling, self-supervised labelling, and the Data Enrichment Platform. • Build workflows for large-scale enrichment, data curation, and hard/rare event mining in support of robust model performance and introspection. • Design and train out-of-distribution detection mechanisms to enable robust driverless operation. • Work at the intersection of model engineering, infrastructure, compute, and data, shaping the boundaries and architecture across those teams. • Use your judgement and expertise to improve the overall efficiency of the machine learning and operations stack. • 🙌 You should apply if • Hands-on experience with ML systems deployed in the real world. • Proficiency in Python and PyTorch, with strong software engineering practices and hands-on experience building reliable machine learning training and evaluation systems. • A strong track record with ML operations and infrastructure, including experience with distributed computing environments and large-scale inference. • Senior-level ownership and collaboration: able to lead a substantial technical area, work across research and engineering boundaries, and bring others along through clear written and verbal communication. • 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 heterogeneous real-world data. • Experience setting up cloud-based monitoring solutions for large-scale operations, including dashboards, logging, and 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 machine learning systems. • 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.