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 development: Post-train, distill, and evaluate GAIA-family large video diffusion models to improve generation quality, controllability, and generalisation. Run ablation studies and investigate failures in visual quality, temporal consistency, motion, and action conditioning.
- Generation at scale: Build and run synthetic video generation pipelines across tens of thousands of driving segments. Optimise inference efficiency, manage GPU resources, and ensure data quality throughout the pipeline.
- From generation to training: Integrate synthetic data across different stages of driving-model training, develop sampling strategies, and measure its impact through evaluation suites and on-road performance.
- Cross-team collaboration: Work with driving-model teams to determine effective data mixtures and with simulation and evaluation teams to turn generated scenarios into useful closed-loop tests.
- Applied research: Translate advances in world modelling, video generation, flow matching, distillation, and data-centric training into measurable improvements in the models and data we deploy.
🧩 What you’ll be working on
- Post-train and iterate GAIA-class world models for synthetic-data capabilities: embodiment 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, artefacts, calibration, and controllability failures that determine whether generated video is training-grade.
- Improve throughput and yield: inference optimisations (distillation, KV cache), 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 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.
At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.
For more information visit Careers at Wayve. https://wayve.ai/careers/ To learn more about what drives us, visit Values at Wayve https://wayve.ai/careers/
DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.