Senior Software Engineer GPU Infrastructure HPC

Skills

About the Role

You will build and operate Kubernetes-based GPU and TPU superclusters across multiple clouds. You will optimize AI training infrastructure, resolve performance and reliability issues, create self-service researcher tools, improve observability and automation, and participate in a 24x7 on-call rotation.

Requirements

• Experience with GPU and TPU clusters
• Experience with distributed training frameworks including JAX, PyTorch, and TensorFlow
• Experience with high-performance computing environments
• Ability to deploy, manage, and troubleshoot Kubernetes clusters for AI workloads
• Proficiency in Python and Go
• Familiarity with Linux internals, RDMA networking, and ML workload performance optimization
• Experience collaborating with AI researchers or ML engineers
• Ability to identify bottlenecks, propose solutions, and drive impact

Responsibilities

• Build and manage Kubernetes-based GPU and TPU superclusters across multiple clouds
• Optimize infrastructure cost efficiency, reliability, and performance for AI workloads
• Identify and resolve infrastructure bottlenecks, performance degradation, and system failures
• Design self-service tools for researchers to monitor, debug, and optimize training jobs
• Translate AI research needs into scalable infrastructure solutions
• Advocate for observability, automation, and infrastructure as code
• Share expertise through code reviews, documentation, and cross-team collaboration
• Participate in a 24x7 on-call rotation

Benefits

• Equity
• Compensation for on-call schedules
• Weekly lunch stipend
• Health and dental benefits
• Mental health budget
• RRSP matching
• 401K
• Pension Scheme
• Parental leave top-up for up to six months
• Arts and culture benefit
• Fitness and wellness benefit
• Workspace improvement credit
• Six weeks of paid vacation
• Travel budget for other offices
• Annual company offsite
• Co-working benefit
• Home office stipend