Location
London (Hybrid working model with in-person collaboration in dedicated office spaces)

Hours
Full Time

Salary
Negotiable

About the Role
At Wayve, we build embodied intelligence that moves real vehicles safely and the ecosystem that a billion machines will run on in the future. As Engineering Manager for the Compute and Data Platform team, you will lead our London-based Compute Foundations team through its next phase of growth. This team builds and operates the large-scale cloud infrastructure that enables our machine learning engineers and researchers to train and run increasingly capable AI models. The platform spans GPU compute, storage, workload scheduling, and supports training and inference across multi-cloud and multi-region environments.

Your leadership will be key in building and maintaining a reliable, high-performance platform that balances availability, performance, capacity, and cost. You will manage a highly technical team working with state-of-the-art tooling to solve infrastructure challenges unique to leading AI companies. Your role includes people leadership, coaching engineers, building technical leadership, hiring and onboarding, project prioritisation, and roadmap planning aligned with company objectives. You will also provide visibility into system health through KPIs and dashboards, and guide complex infrastructure decisions in partnership with technical leads, stakeholders, and cloud providers.

Key responsibilities include leading and developing the team, driving the roadmap and delivery, scaling cloud compute infrastructure, improving scheduling and availability of heterogeneous GPU resources, planning capacity and cost, and ensuring efficient distributed storage and caching strategies across clouds.

Wayve is pioneering an end-to-end AI platform for autonomous driving that learns directly from real-world experience. Our technology enables vehicles to navigate complex urban environments with intelligence and precision, aiming to make autonomy universal. Joining Wayve means contributing to a fast-evolving, high-impact mission in a dynamic and collaborative environment.

Experience
- Proven track record managing and developing infrastructure, platform, or distributed systems engineering teams, including hiring, coaching, performance management, and career development.
- Experience delivering through others by owning team priorities and balancing delivery with operational reliability and long-term platform improvements.
- Substantial experience with Kubernetes and workload queuing and scheduling systems such as Kueue or equivalent.
- Strong understanding of availability, capacity, performance, and cost trade-offs across complex cloud infrastructure involving compute, storage, and data movement.
- Knowledge of large-scale GPU compute infrastructure for machine learning training or inference and operational challenges scaling beyond small GPU deployments.
- Ability to earn engineers’ trust through sound technical judgement without being the primary individual contributor.
- Successful track record as an individual contributor.
- Clear communication skills with technical teams, stakeholders, and external providers, with the ability to translate ambiguous requirements into practical priorities.
- Ability to work from the London office and lead a team based there.

About You
- Strong people leadership skills with a focus on coaching, feedback, and career development.
- Skilled at building technical leadership and empowering engineers with ownership.
- Experienced in hiring and onboarding to grow a high-performing team.
- Strategic thinker able to align team priorities with broader company objectives.
- Comfortable working in a fast-evolving environment where processes and structures are still being developed.
- Passionate about autonomy and keen to learn and grow within the role.

Qualifications
The following are useful but not required:
- Experience operating multi-cloud or multi-region platforms for large-scale AI workloads.
- Experience introducing new GPU architectures or managing heterogeneous GPU fleets.
- Knowledge of distributed storage, caching, and data-locality strategies for data-intensive workloads.
- Experience with capacity forecasting, cloud cost modelling, and collaboration with cloud infrastructure providers.