Applied Scientist, Wayve Labs

Wayve•Vancouver, BC

2d•Hybrid

About The Position

Wayve is seeking Applied Scientists to join Wayve Labs and contribute to the development of next-generation AI systems for autonomous driving. This role involves working at the intersection of machine learning, simulation, robotics, and real-world deployment, focusing on core innovations in embodied AI. The team operates with a long-term perspective, prioritizing multi-year breakthroughs. Key research areas include World & Reward Modeling, Representation Learning & Spatial Intelligence, Scalable Decision-Making Systems, and Cross-Embodiment and Multimodal Learning.

Requirements

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

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

Benefits

Attractive compensation with salary and equity
Immersion in a team of world-class researchers, engineers and entrepreneurs
A unique position to shape the future of autonomy and tackle the biggest challenge of our time
Bespoke learning and development opportunities
Relocation support with visa sponsorship
Flexible working hours
Onsite chef
Workplace nursery scheme
Private health insurance
Therapy
Daily yoga
Onsite bar
Large social budgets
Unlimited L&D requests
Enhanced parental leave