Wayve Principal Research Scientist at Wayve leading research on multimodal reasoning and embodied AI for autonomous driving. Develop new architectures and learning methods to improve decision-making in physical environments.
Responsibilities
As a Principal Research Scientist, you will guide the technical direction of the team, mentor researchers, build strong collaborations with Engineering, and turn original ideas into rigorous evidence, influential publications and capabilities that shape Wayve's next generation of AI.
🧩 What you’ll be working on:
Advance multimodal foundation models: Research and develop LLMs, VLMs and VLAs that integrate visual understanding, language, reasoning and action for embodied decision-making.
Develop reasoning and planning capabilities: Explore structured and latent reasoning, long-horizon planning, temporal understanding, memory, self-reflection and test-time computation for physically grounded agents.
Push the boundaries of model training: Develop scalable training and post-training methods, including supervised fine-tuning, reinforcement learning, reward modelling, distillation and learning from human or AI-generated feedback.
Build steerable and adaptable policies: Enable embodied agents to understand instructions, follow goals and preferences, reason about constraints and generalise to novel environments and situations.
Explore agentic and hierarchical architectures: Investigate how deliberative reasoning, memory, external tools and fast reactive policies can work together, balancing intelligence, reliability and real-time constraints.
Bridge reasoning and action: Develop approaches that turn semantic understanding and reasoning into meaningful improvements in physical-world behaviour, rather than treating language generation as an end in itself.
Drive research from idea to evidence: Design rigorous experiments, build prototypes, establish challenging evaluations and demonstrate improvements in real-world capabilities.
Shape the research direction: Work closely with researchers across foundation models, representation learning, world modelling and reinforcement learning. Contribute original ideas, mentor others and help define the next generation of embodied AI systems.
🙌 You should apply if:
A strong research track record in deep learning, particularly in one or more of LLMs, VLMs, VLAs, multimodal foundation models or embodied AI.
