Technical Lead Manager, Synthetic Data

Overview

In this role you drive the synthetic-data initiative within Wayve’s Simulation team, shaping how generative world models and synthetic data reduce real-world data needs. You will lead a high-performing ML engineering team, setting direction for post-training, conditioning, and scalable data generation. You’ll bridge research and production to land synthetic data in driving-model training and evaluate impact across geographies and vehicle platforms. This is a hands-on leadership role that blends technical depth with people leadership to accelerate AV2.0.

Responsibilities

• Define the technical direction for post-training and conditioning world models for synthetic-data capabilities (rig transfer, pose transfer, controllability)
• Own end-to-end data generation loop: checkpoint/config, large-scale GPU inference, artefacts landing in driving-model training with reproducible lineage
• Lead from the front on core components, codebases and experiments
• Improve throughput and yield: optimize inference (distillation, few-step sampling, KV caching, step count) and enable self-serve synthetic data workflows
• Challenge assumptions to push beyond incremental gains
• Lead a cross-functional ML team spanning generative modelling, generation infrastructure and training
• Coordinate quarterly planning in a high-ambiguity setting and align with OEM timelines
• Build resilient teams through hiring, operating models and inclusive culture
• Mentor team members and provide clear feedback and growth plans
• Navigate evolving research priorities while maintaining clarity and momentum

Key requirements

• 5+ years in ML engineering or applied research with training and shipping neural networks
• 4+ years of people management experience with cross-functional ownership
• Deep knowledge of generative modelling (diffusion, flow matching, autoregressive, or VAEs) for video or high-dimensional temporal data
• Hands-on experience with video, generative or world models (video generation, novel-view synthesis, neural rendering, controllable generation)
• Working knowledge of cameras and 3D geometry (multi-camera rigs, intrinsics/extrinsics, warps, reprojection)
• Experience taking generated data into a downstream model and measuring impact
• Experience operating generation/training at scale (multi-GPU, workflow orchestration, large video artefacts)
• Strong Python and PyTorch fundamentals and building production-like tools
• Excellent communication and mentoring skills
• Ability to balance technical depth with people leadership and to navigate ambiguity

• strong communication
• mentoring and coaching
• openness to ambiguity
• generative modelling (diffusion, flow matching, autoregressive, VAEs)
• video generation and novel-view synthesis
• neural rendering and controllable generation

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