Wayve
Applied Scientist/Machine Learning Engineer, Gaia
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Machine Learning Engineer
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.
About our Simulation Teams
Generative Simulation is the team advancing our end-to-end autonomous driving research, incubating and investing in new ideas that can become game-changing technological advances for Wayve. As a Machine Learning Engineer in the team, you will play a key role in developing next-generation world models and planners, such as GAIA, that can simulate complex, diverse, and temporally consistent driving environments to power faster training, broader testing, and scalable deployment, even in areas and scenarios we have never driven in before. As we push toward the next generation of GAIA, efficiency and interactivity are a major focus: models must run thousands of roll-outs per second, support closed-loop agent interaction, and fit within practical compute budgets. You will work at the intersection of machine learning research, multi-modal modelling, and real-world deployment, tackling questions such as how we can deploy autonomous vehicles in a new geography without collecting any real-world data, and whether synthetically generated environments can fully replace physical testing and data collection.
Your day-to-day
- Setting technical direction: Set technical direction and designing key components of the system.
- Deep work / coding: You spend a focused block implementing and debugging new modeling capabilities.
- Cross-team collaboration, brainstorming and pairing: You brainstorm ideas with your team mates or pair to solve a difficult problem.
- Mentorship / async review: You review a teammate’s pull request or experimental design and outcomes and provide feedback.
- Sync-ups: Share findings with the rest of your team leading to a discussion on designing the next experiments. You align on shared priorities for the week and blockers.
- Paper reading/ reading groups: Read one of the latest papers and summarise them for yourself or the team to present in the next reading group if interesting.
What you’ll be working on
- Invent next-generation, efficient generative world-models (diffusion, transformer or hybrid) that deliver real-time roll-outs and controllable scene editing.
- Architect interactive world models where agents (or humans) can step the model, enabling reinforcement learning, planning and safety evaluation loops.
- Optimise end-to-end performance – from latent compression to context pruning – your aim is to reduce inference latency by orders of magnitude.
- Define robust metrics for long-horizon coherence, physics fidelity and planner integration; run ablations and scaling studies to understand trade-offs.
- Ship impact: integrate your models into closed-loop training and evaluation, and measure the sim-to-real gap against on-road driving-model results.
- Mentor and influence: guide junior researchers, shape technical road-maps, publish at top venues and represent Wayve in the community.
- Challenge assumptions and drive innovation: propose bold ideas, conduct ablation studies, and question conventional approaches to training and evaluation.
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You should apply if
- 4+ years of experience in ML research/engineering with a focus on generative video, world models.
- Deep knowledge in diffusion & latent-video models; track record of improving sampling efficiency or model throughput.
- Experience working with high-dimensional temporal or spatial-temporal data (e.g., video, multi-sensor fusion).
- Strong Python and PyTorch engineering fundamentals, and experience building research-grade production tools.
- Strong publication record or contributions to open-source ML tooling.
- Ability to work collaboratively in a fast-paced, innovative, interdisciplinary team environment.
🌱 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.
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The Interview Process
Our process is clear and respectful of your time:
- Initial call / recruiter screen (30 mins)
- Competency Interviews (Programming/Pytorch debugging and hiring manager interview; 2 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. To learn more about what drives us, visit Values at Wayve
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.
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