At a Glance
- Tasks: Shape the reliability of large-scale AI systems and GPU compute infrastructure.
- Company: Join Wayve, a pioneering tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Dynamic environment with potential leadership opportunities as you grow.
- Why this job: Be a founding member of the Cloud SRE team and make a real impact.
- Qualifications: Experience in SRE roles, Kubernetes, and cloud platforms like AWS or GCP.
The predicted salary is between 70000 - 90000 € per year.
Requirements
- 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) Familiarity with infrastructure-as-code (e.g. Terraform) and secure cloud production environments
- (Desirable) Experience defining and running SLOs/SLIs and building reliability programs across multiple teams
- (Desirable) Experience as an early or founding SRE hire establishing processes from scratch
- (Desirable) Interest in helping shape and grow a Cloud SRE function, with potential to take on leadership responsibilities over time
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.
What the job involves
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.
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
Staff Cloud Site Reliability Engineer (AI/ML Platform & GPU Compute) employer: Deepstreamtech
At Wayve, we pride ourselves on being an innovative employer that empowers our employees to shape the future of AI and cloud infrastructure. Our collaborative work culture fosters creativity and growth, offering unique opportunities for professional development as you help build and scale our cutting-edge AI systems. Located in a vibrant tech hub, we provide a dynamic environment where your contributions directly impact the reliability and performance of large-scale GPU compute platforms, making your work both meaningful and rewarding.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Cloud Site Reliability Engineer (AI/ML Platform & GPU Compute)
✨Tip Number 1
Network, network, network! Get out there and connect with folks in the industry. Attend meetups, webinars, or even online forums related to AI and cloud reliability. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those involving Kubernetes, MLOps, or GPU environments. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your troubleshooting skills. Be ready to discuss how you've tackled complex distributed systems issues in the past. Use real-life examples to demonstrate your problem-solving abilities and your experience with observability stacks.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing passionate candidates who are eager to shape the future of AI and cloud infrastructure. Your unique skills could be just what we need to build a top-notch SRE function!
We think you need these skills to ace Staff Cloud Site Reliability Engineer (AI/ML Platform & GPU Compute)
Some tips for your application 🫡
Show Off Your Experience:Make sure to highlight your experience in SRE or cloud reliability roles. We want to see how you've tackled large-scale systems and any hands-on work with GPU-backed environments or ML infrastructure you've done.
Be Specific About Your Skills:When listing your skills, be specific! Mention your Kubernetes experience, any production workloads you've managed in AWS, GCP, or Azure, and your proficiency in scripting languages like Python or Go. We love details!
Communicate Clearly:Your communication skills matter! Share examples of how you've led incidents or written postmortems. We’re looking for someone who can influence teams and prioritise reliability improvements, so don’t hold back on your achievements.
Apply Through Our Website:We encourage you to apply through our website. It’s the best way for us to get your application and ensure it reaches the right people. Plus, it shows you're keen on joining our team at StudySmarter!
How to prepare for a job interview at Deepstreamtech
✨Know Your Stuff
Make sure you brush up on your experience with SRE, production engineering, and cloud reliability. Be ready to discuss specific projects where you've operated GPU-backed environments or large-scale ML infrastructure. Highlight your hands-on experience with Kubernetes and any production workloads you've managed in AWS, GCP, or Azure.
✨Showcase Your Troubleshooting Skills
Prepare to share examples of how you've tackled complex issues in distributed systems. Think about times when you had to dive deep into networking, storage, or performance problems at scale. Being able to articulate your troubleshooting process will impress the interviewers.
✨Communicate Clearly
Since this role involves leading incidents and writing postmortems, practice explaining technical concepts in a clear and concise manner. You might be asked to describe how you would handle an incident, so think through your communication strategy and how you can influence teams to prioritise reliability improvements.
✨Demonstrate Your Passion for Automation
Talk about your experience with scripting languages like Python or Go, especially in the context of automation. Share any projects where you've implemented self-healing patterns or improved CI/CD processes. Showing your bias towards automation will resonate well with the team looking to build efficient and reliable systems.
