Applied Scientist, Wayve

• 3–5 years of experience post-PhD, or equivalent experience in industry with a focus on dataset design, multi-modal learning, or pretraining
• Strong applied research track record, including publications in venues such as NeurIPS, ICML, ICLR, CoRL, CVPR, or similar
• Hands-on experience curating, filtering, and scaling large datasets (vision, video, or multi-modal) for machine learning models
• Proficiency with machine learning frameworks (e.g., PyTorch) and large-scale data processing (e.g., Ray, Spark, Flyte and other distributed compute infrastructure)
• Ability to design and run scientific experiments to evaluate dataset impact on model performance
• Strong communication and collaboration skills, with experience working across technical and cross-functional teams

Desirable

• We’re looking for a senior Applied Scientist to lead the design and experimentation of Wayve’s core pretraining datasets for foundation models in embodied AI.
• This is a high-impact opportunity to shape the data that drives model generalization across cities, embodiments, and reasoning tasks
• You'll work across research, data, and engineering teams to build world-class multi-modal datasets and define how we evaluate and improve them at scale
• Lead the design, curation, and analysis of large-scale pretraining datasets for embodied AI
• Develop and run data-centric experiments to optimize training effectiveness and generalization
• Collaborate with cross-functional teams in London, Sunnyvale, and Vancouver on data architecture and strategy
• Define and implement metrics and benchmarks to assess dataset quality and model-data interactions
• Contribute to research outputs, tools, and processes that elevate data as a core lever for progress