About the job
We’re looking for Research Scientists to join Wayve Labs and help build the next generation of AI systems for autonomous driving. You’ll work at the intersection of machine learning, simulation, robotics, and real-world deployment, contributing to core innovations that push the boundaries of embodied AI.
Responsibilities
Develop World Models and Planners (e.g., diffusion-based, autoregressive, or hybrid approaches) for realistic and consistent simulation
Advance Reinforcement Learning and Reward Modeling, building scalable and safe learning frameworks across real and synthetic data
Develop Geometric Foundation Models for 3D spatial understanding in dynamic, real-world environments.
Enable Cross-Embodiment Robotics, leveraging the power of multimodal foundation models to accelerate robotic learning on diverse platforms.
Conduct empirical research on Scaling laws, Generalisation, and Sim-to-real transfer
Define and evolve Evaluation Frameworks and Benchmarks for long-horizon prediction, scene fidelity, and driving performance
Qualifications
Minimum
3+ years of experience developing and deploying ML systems in real-world or production settings
PhD, Master’s degree, or equivalent experience in Machine Learning, Computer Vision, Robotics, or a related field
Deep expertise in one or more core Embodied AI areas, such as:
- Foundation models (e.g., transformers, MoE, large-scale training)
- Generative world modeling (e.g., diffusion, autoregressive, hybrid approaches)
- Reinforcement learning (e.g., offline RL, RLHF, reward modeling)
- Spatial AI (e.g., SLAM/SfM, depth estimation, multi-view geometry with multimodal sensors)
Track record of publications at top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL)
Strong programming skills in Python, with experience using frameworks such as PyTorch
A data-centric mindset, with experience working on large-scale datasets and evaluation
Strong problem-solving ability and the ability to collaborate effectively in interdisciplinary teams
Preferred
Experience in autonomous driving, robotics, or simulation systems
Familiarity with large-scale training (e.g., FSDP, DeepSpeed, JAX)
Experience with sim-to-real transfer or data-efficient learning
Contributions to open-source ML tools or research infrastructure