- 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 productive 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

- 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

Benefits

- Private healthcare: Choose our optional health insurance for comprehensive coverage for you and your family.

- Paid time off: Paid vacation plus public holidays and additional leave programs, ensuring you have time to unwind.

- Mental health resources: Through Spill, you can access therapy and mental health support.

- Community and socials: Join clubs or attend team socials to connect over hobbies, sports, or just for fun.

- Competitive compensation: Our compensation package includes cash and equity, making you a true partner in our success.

- Learning and development: Budgets for books, courses, and company-wide training to support your continuous growth.




- Track record of publications at top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL)

- Spatial AI (e.g., SLAM/SfM, depth estimation, multi-view geometry with multimodal sensors)

- 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

- Reinforcement learning (e.g., offline RL, RLHF, reward modeling)

- Foundation models (e.g., transformers, MoE, large-scale training)

- Strong programming skills in Python, with experience using frameworks such as PyTorch

- 3+ years of experience developing and deploying ML systems in real-world or production settings

- Strong problem-solving ability and the ability to collaborate effectively in interdisciplinary teams

- Generative world modeling (e.g., diffusion, autoregressive, hybrid approaches)

- A data-centric mindset, with experience working on large-scale datasets and evaluation

- We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply

- Experience in autonomous driving, robotics, or simulation systems

- Experience with sim-to-real transfer or data-efficient learning

- Familiarity with large-scale training (e.g., FSDP, DeepSpeed, JAX)

- Contributions to open-source ML tools or research infrastructure

📌 Research Scientist (Wayve Labs) (Vancouver)
🏢 Wayve
📍 Vancouver