Site Reliability Engineer

Team: Engineering

Location: Toronto

Commitment: Full-time

Workplace Type: remote

Responsibilities

• Design, operate, and improve reliable infrastructure for AI training and inference workloads
• Own and automate operational workflows across one or more core areas: networking, compute allocation, storage, GPU/server configuration, or AI platforms
• Build monitoring, alerting, runbooks, and incident-response practices that make systems easier to operate
• Diagnose performance, capacity, and reliability issues across hardware, operating systems, networks, schedulers, and distributed workloads
• Partner closely with ML, research, and platform teams to translate workload needs into practical infrastructure improvements
• Improve provisioning, configuration management, testing, and deployment automation
• Help plan cluster growth, capacity allocation, upgrades, and lifecycle management
• Contribute to a thoughtful reliability culture through documentation, post-incident learning, and pragmatic engineering standards

Minimum Qualifications

4+ years of experience in site reliability engineering, infrastructure engineering, systems engineering, or a related production-operations role
Strong hands-on expertise in at least one of the following:
Networking, including firewalls, switching, routing, ASN/BGP configuration, or InfiniBand
Cluster and systems allocation with Kubernetes, SLURM, MAAS, or similar platforms
Distributed storage, particularly Ceph
GPU and server administration, including CUDA drivers, firmware, BIOS, and hardware troubleshooting
AI training or model-serving infrastructure
Experience operating production systems with a focus on availability, performance, security, and automation
Strong Linux administration and scripting skills
A systematic approach to troubleshooting across multiple layers of a complex system
Clear written and verbal communication skills, including the ability to work effectively with a distributed team

Preferred Qualifications

• Experience supporting GPU-intensive AI or HPC environments
• Experience with NVIDIA GPUs, CUDA, NCCL, and high-performance interconnects - Experience with InfiniBand, RDMA, RoCE, or 100Gb+ Ethernet
• Familiarity with Kubernetes, SLURM, MAAS, Terraform, Ansible, or similar infrastructure tooling
• Experience operating or tuning Ceph clusters
• Familiarity with observability tooling such as Prometheus, Grafana, and centralized logging systems
• Experience with hardware provisioning, firmware management, and bare-metal automation
• Experience running large-scale distributed training or high-throughput inference workloads
• Familiarity with cloud and hybrid infrastructure across AWS, GCP, or Azure