At a Glance
- Tasks: Shape the reliability of large-scale AI systems and GPU compute infrastructure from the ground up.
- Company: Join Wayve, a pioneering tech company at the forefront of AI innovation.
- Benefits: Enjoy a hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Inclusive culture that values diverse perspectives and encourages personal development.
- Why this job: Be a founding member of the Cloud SRE team and make a real impact in AI technology.
- Qualifications: Experience in SRE roles, cloud systems, and strong Kubernetes skills are essential.
The predicted salary is between 80000 - 100000 € per year.
This is a rare opportunity to be a founding Staff SRE shaping the reliability of large-scale AI systems and GPU compute infrastructure from the ground up. As a Staff Cloud Site Reliability Engineer at Wayve, you will build and scale the reliability foundations of our AI cloud platform. This includes our Model Development Platform (powering end-to-end model development from raw data to on‑road experimentation) and our GPU Compute platform (large-scale, multi-tenant GPU fleets and scheduling systems driving model training and inference at scale). This is a founding Cloud SRE role. You won’t inherit a mature SRE function, you’ll help create it. You will define the frameworks, automation, and operational standards that ensure our model development infrastructure, distributed systems, and large compute clusters operate predictably, efficiently, and at scale. This role sits at the intersection of AI research, large-scale cloud infrastructure, and production operations. Your work will directly enable faster model training, reliable experimentation, and scalable AI deployment by ensuring our cloud infrastructure is resilient and performant.
Key responsibilities
- Reliability & Platform Ownership
- Own the reliability, availability, and performance of the Model Dev Platform and GPU Compute environments.
- Define and operationalise SLOs, SLIs, and error budgets across platform services.
- Improve capacity planning, scaling strategies, and resource efficiency across large GPU‑backed clusters.
- Partner with ML, platform, and software teams to establish clear production readiness standards.
- Incident Response & On-Call
- Participate in a 24/7 on‑call rotation as first‑line response for cloud and cluster‑related incidents.
- Lead incident triage, escalation, communications, and root cause analysis.
- Translate post‑incident learning into durable architectural or automation improvements.
- Continuously reduce alert noise and recurring operational burden.
- Observability & Operational Excellence
- Design and operate monitoring, logging, tracing, and alerting systems that enable rapid detection and recovery.
- Build dashboards that reflect real user‑centric platform health (not just infrastructure metrics).
- Improve deployment safety through better change management, validation, and rollback mechanisms.
- Automation & Tooling
- Build automation for cluster operations, training workflows, remediation, and scaling tasks.
- Implement self‑healing patterns and resilient recovery workflows.
- Harden CI/CD and release processes to improve deployment safety and velocity.
- Support infrastructure‑as‑code and policy‑driven guardrails to ensure secure, reliable cloud environments.
About you
In order to set you up for success as a Cloud Site Reliability Engineer at Wayve, we’re looking for the following skills and experience.
Essential skills
- Proven experience in an SRE, Production Engineer, or Cloud Reliability role supporting large‑scale cloud systems.
- Experience operating GPU‑backed environments or large-scale ML infrastructure.
- Experience running model training or inference pipelines in production (MLOps).
- Strong Kubernetes experience, including operating production clusters.
- Hands‑on experience running production workloads in AWS, GCP, or Azure.
- Experience operating complex distributed systems in production, ideally including compute‑heavy or high‑performance workloads.
- Experience working with large compute clusters; exposure to AI/ML training or inference workloads strongly preferred.
- Strong Linux fundamentals and proficiency in at least one scripting or systems language (e.g. Python, Go, C++) with a bias toward automation.
- Deep troubleshooting skills across networking, storage, distributed systems, and performance at scale.
- Experience designing and operating observability stacks (e.g. Datadog, Prometheus, Grafana, OpenTelemetry).
- Clear communication skills, including leading incidents, writing postmortems, and influencing teams to prioritise reliability improvements.
Desirable skills
- Familiarity with infrastructure‑as‑code (e.g. Terraform) and secure cloud production environments.
- Experience defining and running SLOs/SLIs and building reliability programs across multiple teams.
- Experience as an early or founding SRE hire establishing processes from scratch.
- Interest in helping shape and grow a Cloud SRE function, with potential to take on leadership responsibilities over time.
Benefits
This is a full‑time role based in our office in London (2 days a week in the office). We operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.
Equal Opportunity Statement
Wayve is committed to creating an inclusive interview experience. If you require accommodations or adjustments to participate fully in our interview process, please let us know. We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self‑driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.
At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.
Staff Cloud SRE – AI/ML Platform & GPU Compute in London employer: Icehouseventures
Wayve is an exceptional employer that fosters a culture of innovation and collaboration, particularly in the dynamic field of AI and cloud infrastructure. With a hybrid working policy that promotes work-life balance and opportunities for professional growth, employees are empowered to shape the future of AI systems while enjoying a supportive and inclusive environment in the heart of London. As a founding Staff SRE, you will not only contribute to cutting-edge technology but also have the chance to define operational standards and frameworks that will drive the company's success.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Cloud SRE – AI/ML Platform & GPU Compute in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Prepare for those interviews! Research the company, understand their tech stack, and be ready to discuss your experience with large-scale cloud systems and GPU environments. Practice common SRE scenarios to show off your problem-solving skills.
✨Tip Number 3
Showcase your projects! Whether it's a GitHub repo or a personal blog, having tangible examples of your work can set you apart. Highlight any automation or tooling you've built that relates to AI/ML platforms.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Staff Cloud SRE – AI/ML Platform & GPU Compute in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience with large-scale cloud systems and GPU environments. We want to see how your skills align with the role, so don’t hold back on showcasing relevant projects!
Show Off Your Technical Skills:When detailing your experience, focus on your hands-on work with Kubernetes, AWS, GCP, or Azure. We love seeing specific examples of how you've tackled challenges in production environments, especially with AI/ML workloads.
Communicate Clearly:Your written application is a chance to demonstrate your communication skills. Be clear and concise, especially when discussing your incident response experience and how you’ve contributed to reliability improvements in past roles.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity to shape our Cloud SRE function from the ground up!
How to prepare for a job interview at Icehouseventures
✨Know Your Stuff
Make sure you brush up on your knowledge of SRE principles, especially around reliability and performance in cloud environments. Familiarise yourself with the specific technologies mentioned in the job description, like Kubernetes and GPU-backed systems, so you can speak confidently about your experience.
✨Showcase Your Problem-Solving Skills
Prepare to discuss past incidents you've managed or challenges you've overcome in production environments. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting how you led incident responses or improved operational processes.
✨Demonstrate Your Automation Mindset
Since automation is key for this role, be ready to talk about any tools or scripts you've developed to streamline operations. Share specific examples of how your automation efforts have improved efficiency or reduced downtime in previous roles.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the company's approach to SRE, their current challenges, and how they envision the future of their AI/ML platform. This shows your genuine interest and helps you assess if the company is the right fit for you.