Applied Scientist/Machine Learning Engineer Gaia
$311kFull-time
Wayve
The role Generative simulation 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 Simulation focusing on unlocking disruptive innovation that solves self-driving. We believe the next leap in autonomy comes from a world model that is faithful enough, and fast enough, to train and evaluate driving models in closed loop — not only to replay logs. As a Machine Learning Engineer in the Simulation team, you’ll play a key role in developing next-generation world models and planners that can simulate complex, diverse, and temporally consistent driving environments. These generative simulation models (like GAIA) will power faster training, broader testing, and scalable deployment—even in areas and scenarios we’ve never driven in before. As we push toward the next generation of GAIA, efficiency and interactivity become a great focus area: models must run thousands of roll-outs per second, support closed-loop agent interaction and fit within practical compute budgets. This role will lead that leap. You’ll work at the intersection of machine learning research, multi-modal modeling, and real-world deployment tackling questions like:
- How can we deploy AVs in a new geography without collecting any real-world data?
- Can synthetically generated environments fully replace physical testing and data collection?
- 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 & 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.
- 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.
- Experience in AVs, robotics, simulation, or other embodied AI domains.
- Experience working with synthetic-to-real transfer.
- 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.
Vacancy posted 24 days ago
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