Staff Software Engineer, Storage Platform

About the role

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here’s what this particular role covers.

🛠️ About our AI Platform Teams

Our AI Platform teams build the infrastructure that powers Wayve’s embodied AI. We store and serve hundreds of petabytes of driving data, growing towards exabyte scale, to support model training, simulation and data pipelines across regions and clouds.

As our first dedicated Staff Software Engineer for Storage Platform, you’ll set the technical direction for how we manage, protect and serve this data. Working closely with Infrastructure, Security, Privacy, Data Engineering and ML teams, you’ll help make training more efficient while reducing one of Wayve’s largest infrastructure costs.

🧠 Your day-to-day

- Lead a hands-on, multi-quarter storage programme, from defining the problems and architecture to delivering platforms that teams adopt.

- Build production services, controllers and infrastructure using Python, Go or a similar language, alongside Kubernetes and Terraform.

- Partner with ML and data teams to understand access patterns, remove storage bottlenecks and reduce the time GPUs spend waiting for data.

- Work with Security and Privacy to strengthen access controls, auditing and safe deletion of vehicle-collected data.

- Write design documents and RFCs, align teams around shared standards, and mentor engineers as the platform and team grow.

🧩 What you’ll be working on

- Data ownership and governance: Give datasets clear owners, purposes and retention classes, with tagging and team-level cost attribution.

- Safe lifecycle management: Build retention, deletion and re-tiering capabilities for Azure Blob, backed by dry runs, audit trails and recovery paths.

- Read performance and caching: Improve throughput and latency by tackling small-object reads, throttling, unnecessary data access and cross-region hops.

- Secure data movement: Design Kubernetes-native NVMe caching and secure replication across regions and clouds.

- Measurable platform outcomes: Establish standards, dashboards and SLOs for availability, performance, cache effectiveness, storage tiers and cost.

🙌 You should apply if

- You’ve operated at Staff Engineer scope, leading complex infrastructure programmes across multiple teams from problem definition through adoption.

- You’ve owned petabyte-scale object storage end to end—including data layout, lifecycle management, performance and cost—with measurable outcomes against a cost target.

- You’ve built scalable systems for data ownership, tagging, cataloguing, retention or deletion.

- You bring strong security and access-management experience, including IAM/RBAC, encryption, secure replication and auditing.