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Wayve is building an AI platform for autonomous driving using end-to-end learning. The company is pioneering an approach where vehicles learn directly from real-world experience to navigate complex urban environments safely and efficiently.
You'll join the Fleet Reliability Engineering team, which ensures the systems supporting Wayve's autonomous vehicle operations are reliable, observable, and scalable. This team works across vehicle software, backend services, infrastructure, networking, and fleet operations to tackle complex reliability challenges spanning both cloud and vehicle-adjacent environments.
Day-to-day responsibilities include:
- Monitoring live system health using logs, metrics, traces, alerts, and reliability dashboards to identify issues before they impact operations
- Investigating incidents end-to-end: forming hypotheses, analyzing data, and driving problems through mitigation to root cause
- Participating in on-call rotation and contributing to post-incident reviews
- Partnering with teams across Wayve to prevent recurring failures
- Building tools and automation to reduce manual operational work and improve issue detection and resolution speed
- Developing meaningful service-level indicators and objectives
- Improving monitoring and alerting systems
- Creating automated checks and remediation workflows
- Influencing system design to turn operational pain points into lasting improvements across software, infrastructure, networking, and fleet operations
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company combines in-person collaboration with remote flexibility. The culture is described as still being written—suited for people who want real ownership rather than a fully-structured environment.
REQUIREMENTS:
- Hands-on experience in Site Reliability Engineering or similar role supporting live production systems
- Strong programming skills in Python, C++, or Rust
- Solid Linux fundamentals
- Experience with cloud platforms, containers, and Kubernetes
- Understanding of observability practices and tools (Datadog, Prometheus, Grafana, OpenTelemetry, or Splunk)
- Knowledge of SLIs, SLOs, and error budgets
- Comfort debugging complex distributed systems across software, networking, operating systems, and hardware-adjacent environments
- Collaborative, detail-oriented approach to incident response and continuous improvement
Note: The posting explicitly welcomes applications from candidates not meeting every requirement if passionate about autonomy and willing to learn.
