Wayve Labs
Automated Driving
AppliedScientist
“Applied Scientist at Wayve Labs. Skills: Embodied AI, Machine Learning, Robotics, Foundation Models, Reinforcement Learning, Spatial AI, World Modeling, Python, PyTorch. 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”
What You'll Achieve.
Enhancing the usability and safety of automated driving systems; Accelerating the transition from assisted to automated driving; Creating autonomy that propels the world forward; Building the next generation of AI systems for autonomous driving; Pushing the frontier of embodied AI; Achieving multi-year breakthroughs; Long-horizon prediction; Scene fidelity; Driving performance
Industry & Context.
Problem-solving ability
Relocation support with visa sponsorship, Hybrid working policy
What They're Looking For.
Must Have
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), programming skills in Python, experience using frameworks such as PyTorch, A data-centric mindset, experience working on large-scale datasets and evaluation, problem-solving ability, ability to collaborate effectively in interdisciplinary teams
Nice to Have
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
What You'll Do.
Develop World Models and Planners (e.g.
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
and Sim-to-real transfer
Define and evolve Evaluation Frameworks and Benchmarks for long-horizon prediction
and driving performance
How You'll Work.
Team & Collaboration
Collaborate effectively in interdisciplinary teams
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