# Engineering Manager - Compute and Data Platform (AI Platform)

**Company**: Wayve
**Location**: London
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://jobs.ashbyhq.com/wayve/6ac4d9d0-f0e2-47a1-b0ef-712c9bde1d4d?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_5d17d078-8ce

## Description

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

As an Engineering Manager - Compute and Data Platform (AI Platform), you'll lead the London-based team through its next phase of growth, helping build and maintain a reliable, high-performance, multi-cloud and multi-region platform.

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'll 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.

Key responsibilities:

- 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.

Requirements:

- Strong track record of managing and developing an infrastructure, platform or distributed systems engineering team.

- Experience with Kubernetes and workload queuing and scheduling systems.

- Ability to reason about availability, capacity, performance and cost across complex cloud infrastructure.

- Understanding of large-scale GPU compute infrastructure for machine learning training or inference.

Nice to have:

- 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.

Benefits:

- Salaries benchmarked against the market annually

- Meaningful equity, sharing in the ownership and long term success of Wayve

- Relocation support and visa sponsorship where applicable

- Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs

- Learning and development budgets with support for training, conferences and growth

- Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more

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## Skills

### Required
- Kubernetes
- GPU compute infrastructure
- cloud infrastructure
- machine learning

### Nice to have
- multi-cloud platforms
- GPU architectures
- distributed storage
- capacity forecasting

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Source: [Apply at jobs.ashbyhq.com](https://jobs.ashbyhq.com/wayve/6ac4d9d0-f0e2-47a1-b0ef-712c9bde1d4d?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)