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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 AI Enablement team builds shared platforms that help people across the company use language models and AI agents safely, reliably, and cost-effectively.
As an early member of this platform team, you will help shape both the technical foundations and the standards for how AI-powered tools are built and operated across Wayve. You'll design, build, and operate infrastructure for model access, agent runtimes, and observability, taking projects from initial problem definition through technical design, deployment, and ongoing operation.
Your day-to-day responsibilities include:
- Designing and building a governed, production-ready access layer for language models, including routing, authentication, cost controls, and auditability
- Creating secure infrastructure and reusable tooling for running agentic workflows in production
- Implementing observability covering platform usage, cost, reliability, and performance
- Establishing governance and safety controls that make AI use secure, compliant, and auditable
- Developing shared primitives, standards, and best practices that enable teams to build and deploy agents without creating one-off infrastructure
- Building reliable, scalable deployments using cloud infrastructure, Kubernetes, and infrastructure-as-code
- Supporting teams adopting the platform by troubleshooting issues, documenting reusable patterns, and helping people understand what's possible with the tools available
You'll partner closely with security, IT, and engineering teams across the organization. Wayve operates a hybrid working model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg, combining in-person collaboration with focused remote work time.
REQUIREMENTS:
- Strong software engineering experience building APIs, services, or developer tooling
- Experience building or operating platforms and infrastructure used by other engineering teams
- Hands-on experience with cloud infrastructure, Kubernetes, and infrastructure-as-code
- Familiarity with LLM APIs and the surrounding ecosystem (model gateways, agent frameworks, MCP, RAG)
- Understanding of production observability, including metrics, tracing, and logging
- Security-conscious approach to authentication, authorization, governance, and auditability
- Comfort taking ownership in an ambiguous, fast-moving environment with clear communication through documentation and collaboration
The posting notes that not meeting every requirement is acceptable if you're passionate about autonomy and keen to learn.
