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Wayve is building an AI platform for autonomous driving. This role leads the Synthetic Data team within the Simulation organization, responsible for turning generative world models (like GAIA-3) into a production engine for training-grade synthetic driving data. The mission is to dramatically reduce dependency on expensive, time-consuming on-road data collection by enabling synthetic data generation, scenario rewriting, and seamless integration into the same training stack used for real driving.
Day-to-day responsibilities include:
- Algorithmic and system design: defining the approach to GAIA generation, designing the synthetic data pipeline and controllability engine with focus on efficiency and quality
- Cross-team collaboration: aligning with driving-model owners, evaluation, and safety teams on utility and scalability of generated data
- Mentorship and code review: providing direct feedback on experimental design, pull requests, and ablation studies
- 1:1s and feedback: building rapport and managing direct reports
- Staying current with state-of-the-art research in video generation, camera transfer, and distillation
- Strategic planning: leading discussions on synthetic data roadmap, safety case, and platform sequencing
Key projects and ownership areas:
- Architect the technical direction for post-training and conditioning world models for synthetic-data capabilities (rig transfer, pose transfer, controllability)
- Own the end-to-end loop from checkpoint and config through large-scale GPU inference to training artifacts with reproducible lineage
- Lead from the front on key components, codebases, and experiments
- Drive inference optimization (distillation, few-step sampling, KV caching) and valid-generation rate improvements
- Build self-serve workflows so model developers can request synthetic datasets without specialist involvement
- Challenge assumptions about where synthetic data delivers value and champion bold ideas
- Lead a high-performing, cross-functional team of ML engineers and applied scientists across generative modeling, generation infrastructure, and training
- Drive quarterly planning and execution in high-ambiguity environments
- Collaborate with world-model researchers, platform/infra engineers, driving-model owners, and evaluation teams to integrate synthetic data into the broader stack
- Manage upwards and laterally to align team goals with company priorities and OEM program timelines
- Grow and structure a resilient team through hiring, effective operating models, and fostering belonging
- Coach and mentor team members with tailored growth plans
- Navigate the team through evolving research priorities while maintaining stability and trust
Wayve operates hybrid working with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company combines in-person collaboration with remote flexibility. The role involves hands-on work in vehicle workshops and labs.
REQUIREMENTS:
- 5+ years of experience in ML engineering or applied research roles, with a track record of training and shipping neural networks (not just operating data platforms)
- 4+ years of people management experience, including direct reports and cross-functional project ownership
- Deep knowledge of generative modeling (diffusion, flow matching, autoregressive, or VAEs) applied to video or other high-dimensional temporal data
- Hands-on experience with video, generative, or world models (e.g., video generation, novel-view synthesis, neural rendering, controllable generation)
- Working knowledge of cameras and 3D geometry (multi-camera rigs, intrinsics/extrinsics, warps, reprojection) and understanding of how they impact generation and downstream training
- Evidence of closing the loop: taking generated or simulated data into a trained downstream model and measuring impact through mix ratios, ablations, and failure analysis
- Experience operating generation or training at real scale (multi-GPU jobs, workflow orchestration, large video artifacts) and making that path reliable
- Strong Python and PyTorch engineering fundamentals, and experience building research-grade production tools
- Excellent communication skills and passion for coaching and mentoring
- Ability to balance technical depth with people leadership, knowing when to lead from the front and when to empower the team
- Comfort embracing ambiguity and helping the team maintain clarity and momentum through uncertainty
