Applied Scientist, World Modeling
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 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. Where you'll have impact: This role would sit within Science focusing on unlocking disruptive innovation that solves self-driving. We believe the next big leap in autonomy won't come from collecting more real-world data-it'll come from learning to simulate the world with unprecedented fidelity and generalization. As an Applied Scientist in the Science team, you'll play a key role in developing next-generation world models and planners that can simulate complex, diverse, and temporally consistent driving environments. These generative simulation models (like GAIA-2) will power faster training, broader testing, and scalable deployment-even in areas and scenarios we've never driven in before. You'll work at the intersection of machine learning research, multi-modal modeling, and real-world deployment-tackling questions like:
- How can we deploy AVs in a new geography without collecting any real-world data?
- Can synthetically generated environments fully replace physical testing and data collection?
- Invent and iterate on generative world models (e.g., diffusion, transformer-based, or hybrid) to simulate dynamic, interactive driving scenarios with high realism and controllability.
- 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.
- Contribute to the broader research community through publications, talks, and open-source contributions.
- 2+ years of experience in ML engineering or applied research roles.
- Deep knowledge of generative modelling (e.g., auto-regressive, diffusion, or VAEs)
- Experience 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.
- Strong publication record or contributions to open-source ML tooling.
- Ability to work collaboratively in a fast-paced, innovative, interdisciplinary team environment.
- Experience in AVs, robotics, simulation, or other embodied AI domains.
- Experience working with synthetic-to-real transfer.
- 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.
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