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For Companies At a Glance
- Tasks: Design and scale AI infrastructure for cutting-edge GPU systems.
- Company: Join a hyper-growth company at the forefront of AI technology.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Make a real impact in AI by solving complex problems with a talented team.
- Qualifications: 5+ years in distributed systems, Kubernetes expertise, and a collaborative mindset.
- Other info: Work in a dynamic environment with significant ownership and career advancement.
The predicted salary is between 36000 - 60000 £ per year.
We’re working with a hyper growth company. They are building the GPU infrastructure to the best AI labs and the biggest enterprise companies. They are building the solution that allows researchers to focus on their models, while utilising the phenomenal scale and reliability of the world’s best AI cloud platform.
The engineering team is small, ambitious, and deeply technical, building the orchestration systems that keep thousands of GPUs running at peak performance across global data centres. This role sits at the heart of it, designing and scaling the systems that make AI at exascale possible.
What You’ll Focus On
- You’ll help shape the orchestration layer for one of the most advanced AI compute environments in the world. Your work will involve:
- Designing core platform services for cluster provisioning, workload orchestration, and resource management APIs.
- Building integrations with schedulers (Kubernetes, Slurm) and container runtimes for reliable, high-performance GPU workloads.
- Developing automation for deployment, imaging, and multi-tenant resource allocation.
- Optimising scheduler performance and resource utilisation across diverse workloads.
- Building lifecycle management and automated remediation systems for large-scale clusters.
- Creating Infrastructure-as-Code modules to support rapid, repeatable deployments across varied environments.
About You
You’re a pragmatic systems builder who thrives in complexity, enjoys autonomy, and understands what it means to own production at scale. You’ll likely bring:
- 5+ years’ experience building distributed systems in Go within cloud-native environments.
- Deep hands-on experience with Kubernetes and container orchestration.
- A strong grasp of Infrastructure-as-Code (Terraform) and configuration management tools (Ansible, Puppet, or similar).
- Experience deploying and operating large-scale GPU clusters or HPC systems.
- Working knowledge of ML infrastructure and familiarity with GPU drivers, CUDA, and container runtimes.
- A low-ego, collaborative approach and a clear, proactive communication style.
In short: This is a role for engineers who like big systems, hard problems, and meaningful ownership. You’ll be joining a team operating at the intersection of software, hardware, and AI.
Staff Software Engineer employer: Motive Group
Contact Detail:
Motive Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff Software Engineer
✨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
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to distributed systems and Kubernetes. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of cloud-native environments and Infrastructure-as-Code. Practice coding challenges and system design questions to impress during the interview.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented engineers like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Staff Software Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the role. Highlight your experience with distributed systems, Kubernetes, and GPU clusters to show us you’re the right fit!
Craft a Compelling Cover Letter: Use your cover letter to tell us why you’re passionate about AI infrastructure and how your background makes you a great candidate. Share specific examples of your work that relate to the job description.
Showcase Your Projects: If you've worked on relevant projects, don’t hesitate to include them! We love seeing real-world applications of your skills, especially in cloud-native environments and automation.
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 from our team!
How to prepare for a job interview at Motive Group
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially Go, Kubernetes, and Terraform. Brush up on your knowledge of distributed systems and be ready to discuss your past experiences with them. This will show that you’re not just familiar with the tools, but you can also apply them effectively.
✨Prepare for System Design Questions
Given the complexity of the role, expect questions around system design and architecture. Practice explaining how you would approach designing a scalable orchestration layer or optimising resource utilisation. Use real-world examples from your experience to illustrate your thought process and problem-solving skills.
✨Showcase Your Collaboration Skills
This role values a low-ego, collaborative approach, so be prepared to discuss how you’ve worked in teams before. Share specific instances where you’ve contributed to group projects or helped resolve conflicts. Highlighting your communication style will demonstrate that you can thrive in their ambitious engineering environment.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions that show your interest in the company’s AI infrastructure and future projects. Inquire about their current challenges with GPU clusters or how they envision scaling their systems. This not only shows your enthusiasm but also helps you gauge if the company aligns with your career goals.
