wayve - Technical Lead Manager, Synthetic Data

• Algorithmic and system design: define approach to GAIA generation, system design of the synthetic data pipeline and controllability engine, focus on efficiency and quality. • Cross-team collaboration: aligning with driving-model owners and with evaluation and safety team on utility and scalability of generated data. • Mentorship / async review: reviewing a teammate’s experimental design, PR, or ablation write-up and giving direct feedback. • 1:1s and feedback: building rapport and managing your direct reports. • Up to date with state of the art: keeping current on video generation, camera transfer, distillation — insofar as it changes a checkpoint or a mix we ship. • Strategy: leading or joining strategic discussions on the synthetic data roadmap, safety case and platform bring-up sequencing. • 🧩 What you’ll be working on • Architect the future — set the technical direction for how we post-train and condition world models for synthetic-data capabilities (rig transfer, pose transfer, controllability), holding a high bar for what counts as training-grade generation. • Own the loop end to end — make sure generation, evaluation and training stay one system: from checkpoint and config, through large-scale GPU inference, to artefacts that land in driving-model training with reproducible lineage. • Get hands-on when it matters — lead from the front on key components, codebases and experiments. • Push throughput and yield — drive inference optimisation (distillation, few-step sampling, KV caching, step count), valid-generation rate, and self-serve workflows so model developers can request synthetic sets without a specialist in the loop. • Disrupt thoughtfully — challenge assumptions about where synthetic data pays off, ask sharp questions, and champion bold ideas that move us beyond incremental gains. • Make things happen — lead a high-performing, cross-functional team of ML engineers and applied scientists working across generative modelling, generation infrastructure and training. Drive quarterly planning and execution in a high-ambiguity environment where the target moves. • Align and connect — collaborate with world-model researchers, platform and infra engineers, driving-model owners and evaluation so synthetic data is integrated into the broader stack, not delivered over a wall. Manage upwards and laterally to align your team’s goals with company priorities and OEM programme timelines. • Architect teams — grow and structure a resilient team by hiring top talent, designing effective operating models, and fostering a sense of belonging regardless of location. Cultivate a strong, inclusive culture rooted in scientific rigour, collaboration and curiosity. • Level up — coach and mentor team members, tailoring growth plans to individual strengths and aspirations. Lead by example through technical engagement and clear feedback. • Champion change — navigate your team through evolving research priorities and fast-moving execution, maintaining stability and trust through uncertainty. • 🙌 You should apply if • 5+ years of experience in ML engineering or applied research roles, with a track record of training and shipping neural networks — not only operating data platforms. • 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 — for example video generation, novel-view synthesis, neural rendering, or controllable generation. • Working knowledge of cameras and 3D geometry (multi-camera rigs, intrinsics/extrinsics, warps and reprojection) and why they break generation or 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 artefacts — and making that path reliable. • Strong Python and PyTorch engineering fundamentals, and experience building research-grade production tools. • Excellent communication skills and a passion for coaching and mentoring others. • You balance technical depth with people leadership. You know when to lead from the front and when to empower your team. • You embrace ambiguity and help your team make sense of it, keeping clarity and momentum through uncertainty. • 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.