Staff Cloud SRE – AI/ML Platform & GPU Compute
Staff Cloud SRE – AI/ML Platform & GPU Compute

Staff Cloud SRE – AI/ML Platform & GPU Compute

Full-Time 70000 - 90000 £ / year (est.) Home office (partial)
Wayve

At a Glance

  • Tasks: Shape the reliability of AI systems and GPU infrastructure from the ground up.
  • Company: Wayve, a leader in Embodied AI technology for automated driving.
  • Benefits: Hybrid working, competitive salary, and a culture that values diversity and inclusion.
  • Other info: Opportunity for career growth and leadership in a dynamic tech environment.
  • Why this job: Join a pioneering team to create impactful AI solutions for the future of driving.
  • Qualifications: Experience in SRE or cloud reliability roles, especially with large-scale systems.

The predicted salary is between 70000 - 90000 £ per year.

About Wayve

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware‑agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast‑paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.

The role

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.

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 post‑mortems, 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.

Working arrangement

This is a full‑time role based in our office in London (2 days a week in the office). At Wayve we operate a hybrid working policy that combines time together in our offices and workshops with time spent working from home. 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.

Equal Opportunity Employer

Wayve is 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. WAYVE is an equal opportunity employer. We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non‑discriminatory.

Staff Cloud SRE – AI/ML Platform & GPU Compute employer: Wayve

Wayve is an exceptional employer that champions innovation and collaboration in the rapidly evolving field of AI technology. With a commitment to diversity and inclusion, employees thrive in a supportive environment that encourages personal and professional growth. Located in London, the hybrid working policy allows for flexibility while fostering teamwork, making it an ideal place for those passionate about shaping the future of automated driving.
Wayve

Contact Detail:

Wayve 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, attend meetups, and connect with current employees at Wayve. A friendly chat can sometimes lead to opportunities that aren’t even advertised!

Tip Number 2

Show off your skills! If you’ve got a portfolio or GitHub with projects related to AI/ML or cloud infrastructure, make sure to highlight it. It’s a great way to demonstrate your expertise beyond just a CV.

Tip Number 3

Prepare for those interviews! Brush up on your SRE knowledge, especially around reliability and automation. Practice explaining complex concepts simply, as communication is key in this role.

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, we love seeing candidates who are genuinely interested in joining us at Wayve.

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

Site Reliability Engineering (SRE)
Production Engineering
Cloud Reliability
GPU-backed environments
MLOps
Kubernetes
AWS
GCP
Azure
Distributed Systems
Linux Fundamentals
Scripting Languages (Python, Go, C++)
Troubleshooting Skills
Observability Stacks (Datadog, Prometheus, Grafana, OpenTelemetry)
Communication Skills

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 and GPU environments, as this will show us you understand what we're looking for.

Showcase Your Skills: Don’t just list your skills—give us examples of how you've used them in real-world situations. Whether it's automating processes or improving system reliability, we want to see how you’ve made an impact in previous roles.

Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon unless it’s relevant. We appreciate a well-structured application that gets straight to the point!

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 the role. Plus, it’s super easy!

How to prepare for a job interview at Wayve

Know Your Stuff

Make sure you brush up on your knowledge of SRE principles, especially in relation to AI/ML platforms and GPU compute. Familiarise yourself with the specific technologies mentioned in the job description, like Kubernetes and cloud services such as AWS, GCP, or Azure. Being able to discuss these confidently will show that you're serious about the role.

Showcase Your Problem-Solving Skills

Prepare to discuss past experiences where you've tackled complex challenges, particularly in large-scale systems. Think of specific incidents where you led incident response or improved reliability. This will demonstrate your ability to handle the fast-paced environment at Wayve and your readiness to contribute from day one.

Ask Insightful Questions

Interviews are a two-way street! Prepare thoughtful questions about Wayve's current projects, their approach to AI/ML, and how they envision the future of their SRE function. This not only shows your interest but also helps you gauge if the company aligns with your career goals.

Emphasise Team Collaboration

Wayve values diversity and teamwork, so be ready to talk about how you've worked effectively in teams before. Share examples of how you've collaborated with ML, platform, and software teams to achieve common goals. Highlighting your communication skills will resonate well with their inclusive culture.

Staff Cloud SRE – AI/ML Platform & GPU Compute
Wayve

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