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Machine Learning Engineer, Controllable GAIA
Wayve
Software Engineering, Data Science
London, UK
Posted on Oct 8, 2025
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.
About us
Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.
Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.
In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.
At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.
Make Wayve the experience that defines your career!
The role
Science is the team that is advancing our end-to-end autonomous driving research. The team’s mission is to accelerate our journey to AV2.0 and ensure the future success of Wayve by incubating and investing in new ideas that have the potential to become game-changing technological advances for the company.
Where you’ll have impact:
This role would sit within Science focusing on unlocking disruptive innovation that solves self-driving. We believe the next leap in autonomy won’t come from collecting endless real-world miles — it will come from simulating the world with unprecedented fidelity and generalisation. That’s where GAIA, our generative world model, comes in.
As an Applied Scientist on the Science team, you’ll play a central role in developing the next generation of GAIA. These controllable world models will roll out diverse, photoreal, and physics-aware futures across multiple sensors (camera, radar, LiDAR), powering faster training, broader testing, and scalable deployment — even in places and situations we’ve never driven before.
GAIA-2 added multi-camera consistency, fine-grained control, and richer geographic diversity, enabling us to stress-test autonomy at scale. The next generation must go further: thousands of real-time rollouts per second, closed-loop interactivity with agents, and compute efficiency that makes training and deployment practical.
You’ll work at the intersection of generative modeling, simulation, and reinforcement learning, tackling questions like:
- How can we deploy AVs in a new geography without collecting any real-world data?
- Can synthetic environments trained with GAIA fully replace physical testing and data collection?
- How do we design controllable models that allow agents to play, explore, and learn safely?
Key responsibilities:
You will be a senior technical contributor inside Science, the team that incubates breakthrough ideas for Wayve. Your mandate:
- Invent next-generation generative world models (diffusion, transformer, or hybrid) that deliver real-time, controllable rollouts.
Architect controllable GAIA models where agents can step into the world, enabling reinforcement learning, planning, and safety evaluation. - Define robust metrics for long-horizon coherence, physics fidelity, and planner integration; run ablations and scaling studies to understand trade-offs.
- Ship impact by integrating models with fleet-scale training, sim-to-real evaluation, and on-vehicle deployment.
- Mentor & influence: guide junior researchers, shape technical roadmaps, publish at top venues, and represent Wayve in the global research community.
- Challenge assumptions: propose bold ideas, run disruptive experiments, and question conventional approaches..
About you
In order to set you up for success as a Senior Applied Scientist at Wayve, we’re looking for the following skills and experience.
- Expertise in ML research/engineering with a focus on generative video, world models.
- Deep knowledge in diffusion & latent-video models
- 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 in AVs, robotics, simulation, or other embodied AI domains.
- Experience working with synthetic-to-real transfer.
Why Join Us
- Work on transformative technology with real-world impact on mobility, safety, and AI.
- Access massive driving datasets, cutting-edge infrastructure, and world-class research talent.
- Be part of a high-trust, high-autonomy team that values creativity, experimentation, and deep thinking.
- Publish, share, and shape the future of generative AI for autonomy.
We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.
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.