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. 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
Science is the team that is advancing our end-to-end autonomous driving research. The team’s mission is to accelerate our journey to AV2.0 and ensure the future success of Wayve by incubating and investing in new ideas that have the potential to become game-changing technological advances for the company. The goal of this role is to build, scale, and optimise next-generation world model architectures (e.g. GAIA and successors) and bridge them into high-throughput training infrastructure, enabling synthetic data and simulation to dramatically accelerate autonomy development. You will live at the intersection of model research, large-scale ML systems, and real-world deployment. You will both invent new generative architectures and make them trainable at scale (efficiently and reliably) so that synthetic environments can exceed reality in utility.
Key Responsibilities
- Design and implement performance improvements (tensor parallelism, pipeline parallelism etc) for large scale training.
- Profile and diagnose large-scale model training jobs to identify bottlenecks (GPU/compute, memory, I/O, communication) and optimise performance.
- Train large-scale temporal models on multi-modal data (video, LiDAR, vehicle telemetry), learning representations of complex real-world dynamics.
- Design experiments to understand model generalization, scaling behavior, and performance trade-offs between synthetic and real data.
- Define and track metrics and benchmarks for long-horizon prediction, scene fidelity, and planner integration.
- Challenge assumptions and drive innovation: propose bold ideas, conduct ablation studies, and question conventional approaches to training and evaluation.
- Collaborate with platform/engineering teams to align research prototypes with production-level infrastructure.
About You
In order to set you up for success as an Applied Scientist at Wayve, we’re looking for the following skills and experience.
- Established background in ML engineering or applied research.
- Hands-on experience optimizing large-scale training workloads (multi-GPU / multi-node), including parallelism, kernel-level optimizations, memory and I/O bottlenecks.
- Proven experience working cross-functionally between research teams and platform / infrastructure teams.
- Demonstrated background working with high-dimensional temporal or spatial-temporal data (e.g., video, multi-sensor fusion).
- Strong Python and PyTorch engineering fundamentals, and experience building research-grade production tools.
- Ability to take bold ideas, run experiments, and iterate quickly.
- Ability to work collaboratively in a fast-paced, innovative, interdisciplinary team environment.
Desirable
- Deep knowledge of generative modelling (e.g., auto-regressive, diffusion, or VAEs)
- Experience in AVs, robotics, simulation, or other embodied AI domains.
Why Join Us
- Work on transformative technology with real-world impact on mobility, safety, and AI.
- Access massive driving datasets, cutting-edge infrastructure, and world-class research talent.
- Be part of a high-trust, high-autonomy team that values creativity, experimentation, and deep thinking.
- Publish, share, and shape the future of generative AI for autonomy.