Technical Lead Manager, Synthetic Data

About us     Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.   Make Wayve the experience that defines your career!   The role  Simulation is advancing our end-to-end autonomous driving research. The team’s mission is to accelerate our journey to AV2.0 by incubating capabilities that become company-level advantages. GAIA, our generative world models, and the synthetic data they produce, are one of those. What this team is solving: This role leads Synthetic Data within Simulation. The team exists to dramatically reduce our dependency on expensive and time-consuming on-road data collection by turning generative world models (models like GAIA-3 ) into a production engine for training-grade experience. When we can restage real driving onto a camera rig that does not exist yet, rewrite ego motion to create scenarios we have never encountered, and land that data in the same training stack we use for real driving, we can train, evaluate and deploy on vehicles and in geographies we have barely collected from. These are some areas the workstream is focused on: Post-training GAIA-class world models for synthetic-data capabilities: rig transfer (new camera and vehicle embodiments), pose transfer (rewritten ego trajectories), and related conditioning on geometry, calibration and actions. Running generation at scale with clear lineage from the model and settings that produced them. Landing synthetic data in driving-model training and proving its impact on suite and on-road metrics. Expanding coverage to new vehicle platforms and safety-critical scenarios, including OEM bring-up and features Modify existing data in a controllable and scalable way to create new scenarios or create new ones from scratch. Where you’ll have impact: As Tech Lead Manager, you’ll lead a high-performing team of machine learning engineers and help us answer questions like: Can we train and validate a driving model for a vehicle platform before the fleet exists? Can synthetically generated data replace scarce real-world data for training and evaluation — and how would we know? How quickly can we deploy autonomous driving in a geography where we’ve never collected AV data? Key responsibilities Technical contributions & leadership As a technical leader you will: 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. Team management & cross-functional execution As a people and execution leader you will: 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 effecti