Machine Learning Engineer (Synthetic Data)

To set you up for success as a MLE at Wayve, we’re looking for the following skills and experience:

4+ years in applied ML / research engineering, with a track record of training and shipping neural nets, not only operating data platforms.

Strong Python and PyTorch (or equivalent); comfort with GPU training, debugging, and reading model code.

Hands-on experience with video, generative, or world models (diffusion / flow-matching / autoregressive video, novel-view synthesis, neural rendering, or similar).

Working knowledge of cameras and 3D geometry (multi-camera rigs, intrinsics/extrinsics, warps/reprojection) and why they break generation or downstream training.

Evidence of taking generated or simulated data into a trained downstream model and measuring impact (mix, ablations, failure analysis).

Ability to operate generation or training at real scale (multi-GPU jobs, workflow orchestration, large video artefacts) and to make that path reliable.

Collaborative, experimental working style with researchers and platform engineers; you will own a capability, not a ticket queue.

World models, video diffusion/flow, or controllable generation (action, pose, camera, text).

Distillation, few-step sampling, KV caching, or other inference-speed work on large generative models.

AV / robotics / simulation; multi-sensor driving data (video, telemetry; LiDAR a plus).

Productionising research: Flyte/Ray/Spark-style jobs, dataset lineage, training mix configuration.

Reward models, offline RL, or closed-loop evaluation of driving policies.

Cloud GPU fleets (Azure/AWS/GCP) and distributed training.

Why Join Us

Shape autonomy through generative simulation. Your models and data will decide whether we can train a new vehicle before the fleet exists.

Work at the frontier of world models. GAIA-scale video generation, camera transfer, pose control, and the training stack that consumes it — with the compute and fleet data to match.

Close the loop to the road. This is not synthetic data for slides. Generated experience already feeds models we take on the road; you will extend that to the next platforms and features.

High-trust, high-autonomy team. You will work with the people who built rig transfer and the generation stack — and be expected to own the next capability.

This is a full-time role based in our office in London. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team.