Applied Scientist/Machine Learning Engineer, Gaia

📋 Job Overview

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 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.

🎯 The Role

🧠 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

✅ Key Responsibilities

• 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.

📌 Required Qualifications

• 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.

⭐ Desirable Experience

• 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.

🎁 Benefits

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.

🛂 Visa & Eligibility

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