Engineering Manager - Compute and Data Platform (AI Platform)

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 Engineering Teams Behind Wayve’s embodied AI is the compute and data infrastructure that enables our ML engineers and researchers to train and run increasingly capable models. Our Compute Foundations team builds and operates the large-scale cloud infrastructure that makes this possible, spanning GPU compute, storage, workload scheduling and the platforms that support training and inference. You’ll lead this London-based team through its next phase of growth, helping build and maintain a reliable, high-performance, multi-cloud and multi-region platform. The challenges go beyond provisioning resources: they include scheduling workloads across different GPU architectures, keeping valuable compute available, and moving data efficiently while managing costs. You’ll lead a team working with state-of-the-art tooling to tackle infrastructure challenges found only at the world’s leading AI companies, building the platform that enables Wayve to develop its AI Driver. 🧠 Your day-to-day People leadership will be your first priority. You’ll coach and develop engineers, set clear expectations, manage performance. You’ll build technical leadership within the team, giving engineers the support and ownership they need to grow. You will guide the growth of the team through hiring and onboarding. Alongside this, you will own project prioritisation and roadmap planning, aligning priorities and delivery plans with stakeholders across Wayve. Your goal is to maximise the impact of the team in alignment with the department and company objectives. You will provide visibility into the state of systems you own through clear KPIs and dashboards. Leading a highly technical team, you’ll bring the technical judgement to guide complex infrastructure decisions and turn competing needs into a clear roadmap. Working with technical leads, partner teams and cloud providers, you’ll balance reliability, performance, capacity and cost, and help the team deliver sustainably. 🧩 What you’ll be working on Leading and developing the team: Coach engineers through regular feedback and one-to-ones, manage performance, support career development and build a healthy, performant and accountable team. Hiring and building technical leadership: Lead hiring and onboarding as the team expands, and develop technical leads who can take ownership of key areas of the platform. Driving the roadmap and delivery: Translate training and inference needs into priorities, align commitments with available resources, and surface risks and dependencies early. Scaling cloud compute: Guide the evolution and operation of a multi-cloud, multi-region training and inference platform, including the adoption of new compute architectures (Nvidia Hopper, Blackwell; Qualcomm). Improving scheduling and availability: Oversee Kubernetes-based infrastructure and queuing and scheduling systems, enabling workloads to use the right resources across heterogeneous GPU environments. Planning capacity and cost: Partner with stakeholders and cloud providers on compute and storage forecasts, accounting for resource lead times, performance requirements and cost trade-offs. Ensuring efficient storage across clouds: Guide distributed storage and caching approaches that improve data access and reduce unnecessary duplication and cross-cloud transfer costs. 🙌 You should apply if You have a strong track record of managing and developing an infrastructure, platform or distributed systems engineering team, including hiring, coaching, performance management and career development. You have delivered through others, owning team priorities and balancing delivery with operational reliability and longer-term platform improvements. You have substantial experience in Kubernetes and workload queuing and scheduling systems, such as Kueue or equivalent technologies. You can reason about availability, capacity, performance and cost across complex cloud infrastructure, including the interaction between compute, storage and data movement. You understand large-scale GPU compute infrastructure for machine learning training or inference and the operational challenges of scaling beyond small GPU deployments. You can earn engineers’ trust through sound technical judgement without needing to be the team’s primary individual contributor. You have successfully delivered as an individual contributor in the past. You communicate clearly with technical teams, stakeholders and external providers, and can turn ambiguous requirements into practical priorities. You are able to work from our London office and lead a team based there. The following experience would be useful but is not required: Operating multi-cloud or multi-region platforms for large-scale AI workloads. Introducing new GPU architectures or managing heterogeneous GPU fleets. Distributed storage, caching and data-locality strategies for data-intensive workloads. Capacity forecasting, cloud cost modelling and close collaboration with cloud infrastructure providers. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision