Applied Scientist/Machine Learning Engineer, Gaia

Overview

In this role you will advance generative simulation for end-to-end autonomous driving by designing efficient world models and planners. You’ll work across ML research, multi-modal modelling, and deployment to enable fast training, broader testing, and scalable real-world use. You’ll push for real-time roll-outs, interactive models, and robust evaluation with a focus on reducing latency and improving sim-to-real alignment. This role offers ownership, collaboration with cross-functional teams, and opportunities to publish and influence Wayve’s technical roadmap.

Pay / Benefits

• competitive salary
• equity
• relocation support and visa sponsorship
• hybrid working with core hours
• learning and development budget
• comprehensive benefits including health/dental/leave

Responsibilities

• Set technical direction for core simulation components
• Implement and debug new modeling capabilities
• Collaborate across teams to brainstorm and pair on tough problems
• Review peers’ experiments and provide feedback
• Share findings with the team and align on weekly priorities
• Read and summarise recent papers for team knowledge sharing
• Invent next-generation world-models that enable real-time roll-outs and controllable scene editing
• Architect interactive world models for reinforcement learning, planning and safety evaluation
• Optimize end-to-end performance to reduce inference latency
• Ship models into closed-loop training/evaluation and measure sim-to-real gaps
• Mentor junior researchers, shape roadmaps, and contribute to publications
• Challenge assumptions and drive innovative training/evaluation approaches

Key requirements

• 4+ years in ML research/engineering focused on generative video or world models
• Deep knowledge of diffusion and latent-video models with efficiency improvements
• Experience with high-dimensional temporal/spatial-temporal data (e.g., video, multi-sensor fusion)
• Strong Python and PyTorch fundamentals; experience building research-grade production tools
• Strong publication record or open-source ML tooling contributions
• Ability to collaborate in a fast-paced, interdisciplinary environment

• collaborative mindset
• ability to mentor and review others’ work
• strong communication and sharing of findings
• diffusion models
• latent-video models
• multi-modal modelling

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