Principal Machine Learning Engineer GAIA
wayve
Overview
You will lead large-scale training and post-training refinement of Gaia, Wayve’s world model, collaborating across research, applications, and core infrastructure. You’ll influence model architecture and translate product needs into improvements for long-horizon prediction and scenario quality. Expect hands-on engineering and mentorship to raise team standards while delivering deployment-ready, reliable models. This role centers on advancing autonomous driving capabilities through scalable, end-to-end model development.
Pay / Benefits- salary benchmarked against market
- meaningful equity
- relocation support and visa sponsorship
- hybrid working with core hours
- learning and development budgets
- comprehensive benefits (health, dental, leave, pension, wellbeing)
- Own and lead large-scale training for Gaia, including experimentation and productionised runs
- Contribute to model architecture decisions and cross-team collaboration
- Translate product and evaluation needs into model improvements
- Mentor others and set technical direction across the team
- Lead Gaia's post-training and closed-loop pipeline with data curation
- Improve autoregressive generation for longer, stable rollouts and deployment readiness
- Engage with research, applications, simulation engineering, and cloud/infrastructure to drive downstream impact
- Provide technical leadership through reviews and high engineering/research standards
- Hands-on experience post-training/fine-tuning large-scale models (language, video, or foundation models)
- Experience with world models, autoregressive and long-horizon generation
- Experience with diffusion/flow models and 3D vision
- Strong model-architecture understanding and ability to contribute to training decisions
- Hands-on engineering in modern ML stacks (e.g., PyTorch) with debugging and reliability focus
- 5+ years of relevant industry experience
- technical leadership
- mentoring
- cross-functional collaboration
- world models
- autoregressive generation
- long-horizon generation
Reference: WJ-747_31160111