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- Tasks: Design and manage Kubernetes clusters for AI training and inference.
- Company: Perplexity, a forward-thinking company in AI infrastructure.
- Benefits: Competitive salary, flexible work options, and opportunities for growth.
- Other info: Dynamic team environment with a focus on innovation and problem-solving.
- Why this job: Join us to shape the future of AI with cutting-edge technology.
- Qualifications: Expertise in Kubernetes, Slurm, and distributed ML training required.
The predicted salary is between 60000 - 80000 £ per year.
Perplexity is seeking an AI Infra Engineer to enhance our AI infrastructure. The role involves designing and managing Kubernetes clusters for training and inference as well as optimizing Slurm-based HPC settings.
Ideal candidates should have expertise in:
- Kubernetes administration
- Slurm workload management
- Experience with distributed training of ML models
This position requires strong problem-solving abilities and experience in maintaining high uptime for AI training and services.
AI Infra Engineer: Scalable Kubernetes & Slurm employer: Perplexity
Contact Detail:
Perplexity Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Infra Engineer: Scalable Kubernetes & Slurm
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and Kubernetes communities. Attend meetups or webinars, and don’t be shy about sliding into DMs on LinkedIn. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Kubernetes and Slurm projects. Whether it’s a GitHub repo or a personal website, let your work speak for itself. This is your chance to shine and demonstrate your problem-solving abilities.
✨Tip Number 3
Prepare for those interviews! Brush up on common questions related to Kubernetes administration and Slurm workload management. Practice explaining your thought process when tackling problems, as this will highlight your expertise and approach to maintaining high uptime.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining us at Perplexity. Tailor your application to reflect your passion for AI infrastructure and how you can contribute to our team.
We think you need these skills to ace AI Infra Engineer: Scalable Kubernetes & Slurm
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Kubernetes and Slurm. We want to see how you've tackled similar challenges in the past, so don’t hold back on those details!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for AI infrastructure and explain why you're the perfect fit for this role. We love hearing about your problem-solving skills and any cool projects you've worked on.
Showcase Relevant Projects: If you've worked on any projects involving distributed training of ML models or optimising HPC settings, make sure to mention them. We’re keen to see real-world examples of your expertise!
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 Perplexity
✨Know Your Kubernetes Inside Out
Make sure you brush up on your Kubernetes knowledge. Be ready to discuss your experience with cluster management, scaling, and troubleshooting. Prepare some examples of how you've optimised Kubernetes for AI workloads in the past.
✨Slurm Mastery is Key
Familiarise yourself with Slurm workload management. Be prepared to explain how you've used Slurm in high-performance computing settings. Think of specific scenarios where you’ve improved job scheduling or resource allocation.
✨Showcase Problem-Solving Skills
Expect questions that test your problem-solving abilities. Prepare to share instances where you faced challenges in maintaining uptime or optimising performance. Use the STAR method (Situation, Task, Action, Result) to structure your answers.
✨Demonstrate Distributed Training Experience
Highlight your experience with distributed training of machine learning models. Be ready to discuss the frameworks you've used and any challenges you overcame. This will show your understanding of the complexities involved in AI infrastructure.