Applied Scientist/Machine Learning Engineer, Gaia

Overview

Join Wayve’s Simulation team as a Machine Learning Engineer focused on next-generation world models and planners like GAIA. You will build efficient generative simulations enabling real-time rollouts, interactive environments, and scalable training for autonomous driving. You’ll bridge ML research, multi-modal modelling, and deployment, addressing sim-to-real challenges and data-light geography expansion. You’ll set technical direction, implement core capabilities, and mentor teammates in a fast-paced, collaborative setting.

Pay / Benefits

• market-competitive salaries
• meaningful equity
• relocation support and visa sponsorship
• hybrid working model
• learning and development budgets
• comprehensive health and dental benefits; parental leave; pension

Responsibilities

• Set technical direction for system components
• Develop and debug new modeling capabilities in focused work blocks
• Collaborate with team through brainstorming, pairing, and knowledge sharing
• Review teammates’ PRs and experimental designs; provide actionable feedback
• Share findings in team syncs and align on weekly priorities and blockers
• Read and summarize recent papers for internal presentations
• Invent efficient generative world-models with real-time roll-outs and controllable scene editing
• Optimize end-to-end performance to reduce inference latency
• Define robust metrics for long-horizon coherence, physics fidelity, and planner integration
• Ship models into closed-loop training and evaluation; assess sim-to-real gap
• Mentor junior researchers and contribute to technical roadmaps
• Propose bold ideas and perform ablations to challenge conventional approaches

Key requirements

• 4+ years of ML research/engineering with a focus on generative video and world models
• Deep knowledge of diffusion and latent-video models; track record of improving sampling efficiency or throughput
• Experience with high-dimensional temporal/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 collaborate in a fast-paced, interdisciplinary environment

• Col collaboration and teamwork
• Mentorship and peer feedback
• Curiosity and drive for innovation
• Diffusion models
• Latent-video models
• Generative video modelling

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