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.

🛠️ About our Simulation Teams

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.

🧠 Your day-to-day

Model work: Post-train or ablate a GAIA rig-transfer or pose-transfer checkpoint (NVS warp, calibration/intrinsics, shortcut/distillation). Inspect failures: black margins, odometry bias, flickering, wrong curvature column.

Generation at scale: Kick off SDS / Flyte jobs for tens of thousands of segments; debug GPU capacity, KV cache, DDIM step count, MCAP/delta-table correctness.

Landing in training: Binarise synthetic into a corpus-compatible table, wire sampling hooks, train RL and read suite + on-road diffs.

Capability expansion: Pose-transfer ELK/AEB sets, dashcam→Alpha3, or a new OEM rig (Nissan Proto 2.1, Gen3, BMW).

Cross-team: Driving-model owners on mix ratios, closed-loop/eval on whether generated MCAPs are usable as suites.

Paper / research alignment: Video generation, novel-view / camera transfer, flow-matching / distillation, data-centric training — only insofar as it changes a checkpoint or a mix we ship.

🧩 What you’ll be working on

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.

🙌 You should apply if

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.

🌱 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.

More about Wayve:

🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines.

Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility.

How we work 💻- Locations & Flexible Working:

Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives.

🔍 The Interview Process:

Our process is clear and respectful of your time:

Initial call / recruiter screen (30 mins)

Competency Interviews (Programming and hiring manager interview; 1.5 hours total)

Deep-dive technical interviews (Systems & domain-specific interviews; 3 hours total)

Final interview : Mission & values alignment (45 mins).

We’ll always explain the format and work around your availability.

What’s in it for you (Location dependant):

• 💰 Salaries benchmarked against the market annually
• 📈 Meaningful equity, sharing in the ownership and long term success of Wayve
• ✈️ Relocation support and visa sponsorship where applicable
• ✅ Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs
• 📚 Learning and development budgets with support for training, conferences and growth
• 🩺 Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more

A quick, honest note before you apply.

Wayve is not a