Research Scientist (Wayve Labs)

Join Wayve Labs as an Applied Scientist and contribute to the development of cutting-edge AI systems for autonomous driving. You will work at the intersection of machine learning, simulation, robotics, and real-world deployment, focusing on areas such as world and reward modeling, representation learning, scalable decision-making systems, and cross-embodiment learning. The position offers competitive compensation, private healthcare, paid time off, mental health resources, and opportunities for learning and development.

• Contribuer à la construction de la prochaine génération de systèmes d'IA pour la conduite autonome, en travaillant à l'intersection de l'apprentissage automatique, de la simulation, de la robotique et du déploiement dans le monde réel.
• Développer des modèles du monde et des planificateurs pour une simulation réaliste et cohérente, et faire progresser l'apprentissage par renforcement et la modélisation des récompenses.
• Conduire des recherches empiriques sur les lois d'échelle, la généralisation et le transfert sim-réalité, et définir et faire évoluer des cadres d'évaluation et des benchmarks.
• Generative world modeling (e.g., diffusion, autoregressive, hybrid approaches)
• 3+ years of experience developing and deploying ML systems in real-world or production settings
• Reinforcement learning (e.g., offline RL, RLHF, reward modeling)
• Foundation models (e.g., transformers, MoE, large-scale training)
• A data-centric mindset, with experience working on large-scale datasets and evaluation
• 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
• Deep expertise in one or more core Embodied AI areas, such as:
• PhD, Master’s degree, or equivalent experience in Machine Learning, Computer Vision, Robotics, or a related field
• Strong problem-solving ability and the ability to collaborate effectively in interdisciplinary teams
• Spatial AI (e.g., SLAM/SfM, depth estimation, multi-view geometry with multimodal sensors)
• 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