Staff Cloud SRE – AI/ML Platform & GPU Compute

Staff Cloud SRE – AI/ML Platform & GPU Compute

Full-Time 70000 - 90000 € / year (est.) No home office possible
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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 with a commitment to diversity and equal opportunity.
  • 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 70000 - 90000 € 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 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 personal 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.

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Contact Detail:

Icehouseventures Recruiting Team

StudySmarter Expert Advice🤫

We think this is how you could land Staff Cloud SRE – AI/ML Platform & GPU Compute

Tip Number 1

Network like a pro! Reach out to folks in the industry, especially those already at Wayve. A friendly chat can open doors and give you insider info on what they're really looking for.

Tip Number 2

Show off your skills! If you've got a project or a GitHub repo that highlights your experience with SRE, AI, or cloud systems, make sure to mention it during interviews. It’s a great way to demonstrate your hands-on expertise.

Tip Number 3

Prepare for technical challenges! Brush up on your troubleshooting skills and be ready to discuss how you've tackled complex issues in the past. They’ll want to see how you think on your feet.

Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the team at Wayve.

We think you need these skills to ace Staff Cloud SRE – AI/ML Platform & GPU Compute

Site Reliability Engineering (SRE)
Cloud Systems Management
GPU-backed Environments
MLOps
Kubernetes
AWS
GCP

Some tips for your application 🫡

Tailor Your Application:Make sure to customise your CV and cover letter for the Staff Cloud SRE role. Highlight your experience with large-scale cloud systems, GPU environments, and any relevant MLOps work. We want to see how your skills align with what we're looking for!

Showcase Your Technical Skills:Don’t hold back on showcasing your technical prowess! Mention your hands-on experience with Kubernetes, AWS, GCP, or Azure, and any scripting languages you’re comfortable with. We love seeing candidates who can demonstrate their deep troubleshooting skills.

Communicate Clearly:When writing your application, clarity is key! Use straightforward language to explain your past experiences, especially when discussing incident response and operational excellence. We appreciate candidates who can communicate complex ideas simply.

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 don’t miss out on any important updates. Plus, it shows you’re keen on joining our team!

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 faced in production environments. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting how you approached problems and what improvements you implemented.

Demonstrate Your Collaborative Spirit

This role involves working closely with ML and software teams, so be ready to share examples of how you've successfully collaborated in the past. Emphasise your communication skills and how you’ve influenced teams to prioritise reliability improvements.

Ask Insightful Questions

Prepare thoughtful questions that show your interest in the role and the company. Inquire about their current challenges in AI/ML infrastructure or how they envision the evolution of the SRE function. This not only demonstrates your enthusiasm but also helps you gauge if the company is the right fit for you.