Engineering Team Lead

Company Description

Run:AI is bridging the gap between data science and computing infrastructure by creating a high-performance compute virtualization layer for deep learning, speeding the training of neural network models and enabling the development of large AI models. By abstracting workloads from underlying infrastructure, Run:AI creates a shared pool of resources that can be dynamically provisioned for full utilization of expensive GPU compute.

Job Description

Run:AI provides organizations with a world-class machine learning platform to improve productivity and efficiency for data scientists. Our product provides a Run:AI unique HPC scheduler, relies on Run:AI advanced GPUs virtualization technology and makes GPUs first class citizens in Kubernetes.
The Platform group at Run:AI is seeking a talented Engineering Team Lead to lead the development of our world-leading scheduler for AI workloads. The ideal candidate is a Kubernetes expert with a proven record of managing talented backend engineers and experience in building enterprise-grade systems with a complex microservices architecture.

Qualifications:

• 3+ years experience as an Engineering team lead.
• Kubernetes expert with 4+ years of experience in advanced cloud-native development, including designing and implementing Custom Resource Definitions (CRDs) with custom operators and controllers, creating admission controllers and webhooks, and possessing deep understanding in Kubernetes networking (CNI plugins, ingress controllers) and storage (CSI drivers) - must.
• Strong algorithmic skills with experience in implementing and optimizing algorithms within existing complex architectures.
• Superb technical level allowing you to mentor team members. Both code and design reviews.
• Work with the team on a daily basis to ensure team members have a clear understanding of deliverables, a comfortable work environment, and effective project delivery using agile practices.
• Experience in developing complex distributed systems running at a very large scale using microservices architecture.
• Fast learner with an ability to dive deep into details of cutting-edge technologies and deliver high quality solutions end-to-end.
• B.Sc in Computer Science, Mathematics or equivalent.