Technical Lead Manager, Synthetic Data

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.

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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.

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.

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?

Technical contributions & leadership

• 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: