wayve - Applied Scientist/Machine Learning Engineer, Gaia

• Setting technical direction: Set technical direction and designing key components of the system. • Deep work / coding: You spend a focused block implementing and debugging new modeling capabilities. • Cross-team collaboration, brainstorming and pairing: you brainstorm ideas with your team mates or pair to solve a difficult problem • Mentorship / async review: You review a teammate’s pull request or experimental design and outcomes and provide feedback • Sync-ups: Share findings with the rest of your team leading to a discussion on designing the next experiments. You align on shared priorities for the week and blockers. • Paper reading/ reading groups: Read one of the latest papers and summarise them for yourself or the team to present in the next reading group if interesting • 🧩 What you’ll be working on • 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 and influence: guide junior researchers, shape technical road-maps, publish at top venues and represent Wayve in the community. • Challenge assumptions and drive innovation: propose bold ideas, conduct ablation studies, and question conventional approaches to training and evaluation. • 🙌 You should apply if • 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. • 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.