Applied Scientist/Machine Learning Engineer Gaia

Job description

Job responsibilities

• 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 next-generation, efficient generative world-models (diffusion, transformer or hybrid) that deliver real-time roll-outs and controllable scene editing.
• Architect interactive world models where agents (or humans) can step the model, enabling reinforcement learning, planning and safety evaluation loops.
• Optimise end-to-end performance – from latent compression to context pruning your aim is to reduce inference latency by orders of magnitude.
• Define robust metrics for long-horizon coherence, physics fidelity and planner integration; run ablations and scaling studies to understand trade-offs.
• Ship impact : integrate your models into closed-loop training and evaluation, and measure the sim-to-real gap against on-road driving-model results.
• Mentor & influence : guide junior researchers, shape technical road-maps, publish at top venues and represent Wayve in the community

Job requirements

• 4+ years of experience in ML research/engineering with a focus on generative video, world models.
• Deep knowledge in diffusion & latent-video models; 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, 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.