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

At Wayve, we teach machines to drive by training end-to-end neural networks that learn from large volumes of real-world data, not by coding rules. That takes a lot of data infrastructure and compute orchestration: our workloads span thousands of GPUs, petabytes of driving data, and training and inference clusters in several regions. Our AI Infrastructure team builds the compute and storage systems behind our model development lifecycle.

Your day-to-day

As Principal Engineer, you'll design and guide how our core compute and storage systems evolve. You'll work across AI, systems and cloud infrastructure, speeding up AI research, enabling fast model deployment and keeping the platform ahead of the company's demands. You'll advise leadership on compute investment and help engineers across the org grow through architectural coaching and technical deep dives.

What you'll be working on

• Global compute strategy: defining and evolving how Wayve allocates and orchestrates training and inference workloads across thousands of GPUs and multiple data centres, for throughput, resiliency and cost efficiency
• Petabyte-scale data federation: designing systems for fast, reliable access to high-volume sensor and simulation data across geographies, and preparing Wayve to operate at exabyte scale
• Cross-region GPU job execution: building the foundations for large-scale AI workloads to run across hybrid and multi-cloud environments
• Cloud infrastructure advisory: helping leadership align compute investment and architecture with company strategy, growth plans and performance goals
• Technical leadership and mentorship: coaching engineers on architecture, leading technical deep dives, and building a culture of operational and engineering excellence

You should apply if

• You have 10+ years designing and building large-scale distributed systems, with at least 4 years focused on GPU-based cloud infrastructure
• You have enabled large-scale AI training, inference or computer vision workloads in GPU clusters
• You understand petabyte-scale data architecture in depth, including storage federation, high-throughput access and data locality for AI workloads
• You have a track record of defining and communicating architectural strategy, balancing long-term vision with delivery needs
• You mentor naturally, with a history of developing engineers and influencing technical direction across teams
• You have an advanced degree in Computer Science, Electrical Engineering or a related field, or equivalent industry experience

Nice to have:

• Experience with multi-cloud orchestration, particularly in latency- or cost-sensitive training and inference pipelines
• Familiarity with systems like Ray, Kubernetes, Airflow or Flyte, and fluency in AI/ML job scheduling, model lifecycle management and infrastructure-as-code
• Background supporting safety-critical or real-time inference use cases (e.g. robotics, autonomous vehicles, aerospace)
• An interest in building infrastructure as a product that gives research and product teams performance and simplicity

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.

How we work - Locations & Flexible Working:

Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives.

The Interview Process:

Our process is clear and respectful of your time:

• Initial call / recruiter screen
• Deep-dive technical interviews [programming, system design & domain-specific interviews; 4 hours total]
• Final interview: mission & values alignment

We'll always explain the format and work around your availability.

What's in it for you (Location dependant):

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

A quick, honest note before you apply.

Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you'll help write it. That suits people who want real ownership more than people who need a settled structure from day one.

If that sounds like the kind of problem you want to spend your time on, we'd really like to hear from you.