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Wayve is building the leading AI platform for autonomous driving, using end-to-end neural networks trained on real-world data rather than hand-coded rules. The company operates a global platform for embodied AI that enables vehicles to learn, adapt, and improve at scale.
As Principal Engineer for Data & Compute, you will design and guide the evolution of Wayve's core compute and storage systems that power AI research and model deployment. You'll work across AI, systems, and cloud infrastructure teams to accelerate research velocity, enable fast model deployment, and keep the platform ahead of company demands.
Key responsibilities include:
- Defining and evolving global compute strategy for allocating and orchestrating training and inference workloads across thousands of GPUs and multiple data centers, optimizing for throughput, resiliency, and cost efficiency
- Designing petabyte-scale data federation systems for fast, reliable access to high-volume sensor and simulation data across geographies, with preparation for exabyte-scale operations
- Building foundations for large-scale AI workloads to run across hybrid and multi-cloud environments with cross-region GPU job execution
- Advising leadership on compute investment and architecture alignment with company strategy, growth plans, and performance goals
- Providing technical leadership and mentorship through architectural coaching, technical deep dives, and building a culture of operational and engineering excellence
Wayve operates hybrid working with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company emphasizes real ownership and is still writing much of its playbook—suited for people who want meaningful influence over how the organization develops.
REQUIREMENTS:
- 10+ years designing and building large-scale distributed systems, with at least 4 years focused on GPU-based cloud infrastructure
- Demonstrated experience enabling large-scale AI training, inference, or computer vision workloads in GPU clusters
- Deep understanding of petabyte-scale data architecture, including storage federation, high-throughput access, and data locality for AI workloads
- Track record of defining and communicating architectural strategy, balancing long-term vision with delivery needs
- Natural mentoring ability with history of developing engineers and influencing technical direction across teams
- Advanced degree in Computer Science, Electrical Engineering, or related field, or equivalent industry experience
NICE TO HAVE:
- Experience with multi-cloud orchestration, particularly in latency- or cost-sensitive training and inference pipelines
- Familiarity with Ray, Kubernetes, Airflow, Flyte, and fluency in AI/ML job scheduling, model lifecycle management, and infrastructure-as-code
- Background supporting safety-critical or real-time inference use cases (robotics, autonomous vehicles, aerospace)
- Interest in building infrastructure as a product for research and product teams
