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Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. The Model Development Platform team builds the infrastructure and tooling behind Wayve's AI model lifecycle, from data ingestion and training to experiment scheduling and on-road testing.
As Principal Engineer, you will own the end-to-end architecture of the platform and keep it reliable, scalable, and coherent. You will partner with the Head of Model Dev Platform to set and execute the technical vision, aligning infrastructure and tooling with company goals. You will lead by example, going deep across web applications, distributed compute, ML Ops, data pipelines, and optimization algorithms. Through architecture and mentorship, you will help teams build platform capabilities that measurably speed up model development and fleet learning.
Key responsibilities include:
- Designing and evolving the platform's architecture for reliability, observability, and scalability; setting performance, latency, and availability targets; and driving engineering standards to meet them
- Unifying the platform across front-end UIs, distributed training, Spark data pipelines, and optimization-based experiment scheduling so systems work together cleanly
- Taking on the hardest problems across subteams, leading architectural reviews, and proposing pragmatic solutions that balance innovation with operational simplicity
- Building systems that optimize how models are tested in simulation and on-road, using techniques like linear programming and heuristic optimization to balance hardware, safety, and research priorities
- Architecting pipelines that ingest, transform, and enrich petabytes of fleet sensor data, and driving efficient compute use across GPU, CPU, cloud, and edge for prototyping and large-scale training
- Working with Product, Research, and Operations to align architecture with user needs and co-owning the platform's long-term roadmap
Wayve operates a hybrid working model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company offers relocation support and visa sponsorship where applicable, learning and development budgets, comprehensive benefits including health insurance, dental, enhanced parental leave, retirement/pension, and access to wellbeing resources.
REQUIREMENTS:
- 10+ years designing and building large-scale distributed systems, ML/AI infrastructure, full-stack web applications, or developer platforms, including at least 3 years as a staff or principal-level engineer
- Experience designing systems spanning web platforms, ML pipelines, and large-scale compute orchestration (e.g., Spark, Ray, Kubernetes, Airflow, MLflow)
- Demonstrated track record driving platform reliability improvements, defining SLAs/SLOs, and building self-healing, observable systems that run at "four nines" availability or better
- Deep understanding of distributed computing, workflow orchestration, data modeling, and API design; ability to write and review production-quality code
- Strong communication skills across functions; ability to guide engineers, managers, and researchers toward a unified technical direction
- Experience mentoring engineers across levels and building a culture of engineering excellence
NICE TO HAVE:
- Experience applying algorithmic or mathematical optimization (e.g., linear programming, graph algorithms) to operational or scheduling problems
- Familiarity with end-to-end model lifecycle tooling, from data ingestion and training CI to model artifact tracking and evaluation workflows
- Prior exposure to autonomous systems, robotics, or other safety-critical domains
- Experience with modern web frameworks (e.g., React, Flask, FastAPI) and how they integrate with backend systems
- Understanding of data privacy, compliance, and secure handling practices for large-scale sensor data
