About us
Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career!
The Role
We’re looking for Applied 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.
Situated within Wayve, we are a high-conviction research team with the strategic patience and backing to prioritise multi-year breakthroughs over incremental gains. We are looking for highly motivated individuals with expertise and passion to push the frontier of embodied AI, including (but not limited to) the following areas:
- World & Reward Modeling: Building realistic, diverse simulators that can predict the consequences and costs of actions.
- Representation Learning & Spatial Intelligence: Advancing how machines truly understand and navigate dynamic, unstructured 3D environments, from detailed spatial understanding, to efficient long term memory.
- Scalable Decision-Making Systems: Designing architectures, reasoning systems, and policy learning algorithms that operate over long contexts, and scale with data and compute.
- Cross-Embodiment and Multimodal Learning: Advance embodied learning systems that can flexibly adapt to diverse robotic platforms and multimodal inputs, using vision, language, and active sensors.
Key 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
What You’ll Bring
Must-haves:
- 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
Nice-to-haves:
- 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