Applied Scientist/Machine Learning Engineer, Gaia
Salary not listed • London, United Kingdom • Full-time
Posted 28d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. This role is within the Generative Simulation team, which advances autonomous driving research by incubating new ideas. As a Machine Learning Engineer, you will develop next-generation world models and planners, such as GAIA, to simulate complex driving environments for faster training, broader testing, and scalable deployment. A major focus is on efficiency and interactivity, requiring models to run thousands of roll-outs per second, support closed-loop agent interaction, and fit within practical compute budgets. You will work at the intersection of machine learning research, multi-modal modeling, and real-world deployment, tackling challenges like deploying autonomous vehicles without real-world data and assessing if synthetic environments can replace physical testing.
Responsibilities
- Set technical direction and design key system components.
- Implement and debug new modeling capabilities.
- Collaborate with and brainstorm ideas with teammates.
- Review pull requests and experimental designs, providing feedback.
- Share findings with the team and align on priorities and blockers.
- Read and summarize AI papers for team discussions.
- Invent next-generation, efficient generative world models (diffusion, transformer, or hybrid) for real-time roll-outs and controllable scene editing.
- Architect interactive world models enabling reinforcement learning, planning, and safety evaluation loops.
- Optimize end-to-end performance to reduce inference latency.
- Define robust metrics for long-horizon coherence, physics fidelity, and planner integration.
- Integrate models into closed-loop training and evaluation, measuring the sim-to-real gap.
- Mentor junior researchers and shape technical roadmaps.
- Propose innovative ideas and conduct ablation studies.
Requirements
- 4+ years of experience in ML research/engineering with a focus on generative video and world models.
- Deep knowledge in diffusion & latent-video models, with a track record of improving sampling efficiency or model throughput.
- Experience working with high-dimensional temporal or spatial-temporal data (e.g., video, multi-sensor fusion).
- Strong Python and PyTorch engineering fundamentals.
- 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.