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About the Role
This role leads Synthetic Data within Simulation, aiming to reduce dependency on on-road data collection by using generative world models to create training-grade synthetic data. The workstream focuses on post-training GAIA-class world models, large-scale generation, integrating synthetic data into training, and expanding coverage to new platforms and scenarios.
Responsibilities
- Architect the technical direction for post-training and conditioning world models for synthetic-data capabilities.
- Ensure generation, evaluation, and training function as a single system with reproducible lineage.
- Lead hands-on development for key components, codebases, and experiments.
- Optimize inference throughput and yield, including inference optimization and self-serve workflows.
- Challenge assumptions and champion bold ideas for synthetic data applications.
- Lead a high-performing, cross-functional team of ML engineers and applied scientists.
- Drive quarterly planning and execution in a high-ambiguity environment.
- Collaborate with researchers, platform engineers, and model owners for seamless integration.
- Manage upwards and laterally to align team goals with company priorities.
- Grow and structure a resilient team by hiring talent, designing operating models, and fostering inclusion.
- Coach and mentor team members, tailoring growth plans to individual strengths.
- Lead by example through technical engagement and clear feedback.
- Navigate the team through evolving research priorities and fast-moving execution.
Requirements
- 5+ years of experience in ML engineering or applied research roles with a track record of training and shipping neural networks.
- 4+ years of people management experience, including direct reports and cross-functional project ownership.
- Deep knowledge of generative modelling (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 and reprojection) and their impact on generation or downstream training.
- Evidence of closing the loop: taking generated or simulated data into a trained downstream model and measuring impact.
- Experience operating generation or training at scale (multi-GPU jobs, workflow orchestration, large video artefacts) and ensuring reliability.
- Strong Python and PyTorch engineering fundamentals.
- Experience building research-grade production tools.
- Excellent communication skills and a passion for coaching and mentoring others.
- Ability to balance technical depth with people leadership.
- Ability to embrace ambiguity and help the team maintain clarity and momentum.
- Experience in AVs, robotics, simulation, or other embodied AI domains (nice to have).
- Experience with multi-sensor driving data (video, telemetry; LiDAR a plus) (nice to have).
- Experience with distillation, few-step sampling, KV caching, or other inference-speed work on large generative models (nice to have).
- Experience with reward models, offline RL, or closed-loop evaluation of driving policies (nice to have).
- Experience productionizing research (e.g., Flyte/Ray/Spark-style jobs, dataset lineage, training mix configuration; cloud GPU fleets, distributed training) (nice to have).
- Strong publication record or contributions to open-source ML tooling (nice to have).
- Previous experience in startup-like or high-ambiguity environments (nice to have).
Skills
- Machine Learning Engineering
- Applied Research
- Neural Network Training
- People Management
- Generative Modelling
- Video Generation
- Novel-View Synthesis
- Neural Rendering
- Controllable Generation
- 3D Geometry
- Python
- PyTorch
- Communication
- Mentoring
- Technical Leadership
- Inference Optimization
- Workflow Orchestration
- Distributed Training
Location
- London
Work Type
- Full-time
- Hybrid
Experience Level
- 5+ years in ML engineering/applied research
- 4+ years in people management
About the Company
- Wayve is advancing end-to-end autonomous driving research.
- The company's mission is to accelerate the journey to AV2.0 by incubating capabilities that become company-level advantages.
- GAIA, their generative world models, and the synthetic data they produce, are key to this advancement.