Machine Learning Engineer (Synthetic Data)

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here’s what this particular role covers.

The role

Simulation is advancing end-to-end autonomous driving research. The team’s mission is to accelerate AV2.0 by incubating capabilities that become company-level advantages — generative world models and the synthetic data they produce are one of those.

The goal of this role is to build, scale, and optimise next-generation world model architectures (GAIA and successors) and bridge them into high-throughput generation and training infrastructure, so synthetic data can dramatically accelerate autonomy development.

You will post-train world models for new embodiments and behaviours (rig transfer, pose transfer, dashcam restaging), generate multimodal synthetic experience at scale, and land that data in the same training stack we use for real driving. You sit between ML research and engineering: collaborating with scientists on architecture and conditioning, and with platform engineers on generation jobs, training artefacts, and how synthetic data is mixed into training.

Your work will decide how fast we can train, evaluate, and deploy driving models on vehicles we have barely collected from.

Key responsibilities:

• Post-train and iterate GAIA-class world models for synthetic-data capabilities: rig transfer (new camera/vehicle embodiments), pose transfer (rewritten ego trajectories), and related conditioning (geometry, calibration, actions).

• Own the generation loop: config → large-scale GPU inference → training-ready artefacts, with clear lineage from the model and settings that produced them.

• Land synthetic data in driving-model training (behaviour cloning, reward models, RL): binarisation, mix ratios, quality filters, and experiments that measure suite and on-road impact — including when synthetic should replace scarce real rig data.

• Diagnose and fix geometry, calibration, and controllability failures (intrinsics/extrinsics, NVS warps, odometry/curvature, flickering, camera-layout artefacts) that determine whether generated video is training-grade.

• Improve throughput and yield: inference optimisations (shortcut, distillation, KV cache, step count), valid-generation rate, and self-serve workflows so model developers can request synthetic sets without a specialist.

• Expand coverage to new vehicle platforms and safety-critical scenarios (OEM bring-up; Emergency Lane Keeping / Automatic Emergency Braking).

• Partner with world-model researchers, infra, and driving-model owners so generation, evaluation, and training stay one system.

About you

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.

Desirable

• 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.

A quick, honest note before you apply.

Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one.

If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you.