Research Scientist, Wayve Labs

Company: wayve
Apply for the Research Scientist, Wayve Labs
Location: London
Job Description:

Overview

In this role you will advance embodied AI for autonomous driving within Wayve Labs, bridging ML, simulation, robotics and real-world deployment. You’ll push multi-year breakthroughs in world modeling, spatial understanding and cross-embodiment learning. You’ll shape evaluation frameworks and contribute to scalable, responsible learning systems. Join a high-conviction research team tackling big, impactful problems with real-world deployment potential.

Pay / Benefits

  • salary and equity
  • relocation support with visa sponsorship
  • hybrid working policy
  • onsite chef
  • private health insurance
  • daily yoga

Responsibilities

  • Develop World Models and Planners for realistic simulation
  • Advance RL and reward modeling with scalable, safe learning across real and synthetic data
  • Develop Geometric Foundation Models for 3D spatial understanding
  • Enable Cross-Embodiment Robotics using multimodal foundation models
  • Conduct empirical research on scaling laws, generalisation and sim-to-real transfer
  • Define and evolve evaluation frameworks and benchmarks for long-horizon prediction and driving performance

Key requirements

  • 3+ years of experience developing and deploying ML systems in real-world or production settings
  • PhD or Master’s degree in Machine Learning, Computer Vision, Robotics, or a related field
  • Deep expertise in Embodied AI areas: foundation models, generative world modeling, RL, spatial AI
  • Track record of publications at top-tier conferences
  • Strong Python programming with PyTorch experience
  • Data-centric mindset with experience on large-scale datasets and evaluation
  • Strong problem-solving and collaboration across interdisciplinary teams
  • collaborative
  • strong communication
  • interdisciplinary teamwork
  • Foundation models (transformers, MoE)
  • Generative world modeling (diffusion, autoregressive, hybrids)
  • Reinforcement learning (offline RL, RLHF, reward modeling)

Posted: September 14th, 2026