About The Role
We're seeking an experienced Network Engineer to design, build, and optimize the high-performance networking infrastructure powering our AI/ML operations in Toronto. You'll work at the cutting edge of network technology—managing InfiniBand and ultra-high-speed Ethernet fabrics that connect NVIDIA H100 and A100 GPUs, over 20PB of Ceph storage, and hundreds of servers.
You'll be hands-on with the full lifecycle of our network infrastructure: planning, building, testing, deploying, and keeping everything running at peak performance. That means troubleshooting issues as they arise, monitoring network performance and throughput, developing automation to streamline operations, and working closely with HPC and ML teams to ensure they have the bandwidth they need. You'll also help us plan for future capacity and evaluate emerging network technologies as we scale to meet increasingly demanding workloads.
If you're a natural problem-solver with a passion for continuous learning, we'd love to hear from you.
- 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
- 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
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Experience supporting GPU-intensive AI or HPC environments
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Experience with NVIDIA GPUs, CUDA, NCCL, and high-performance interconnects - Experience with InfiniBand, RDMA, RoCE, or 100Gb+ Ethernet
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Familiarity with Kubernetes, SLURM, MAAS, Terraform, Ansible, or similar infrastructure tooling
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Experience operating or tuning Ceph clusters
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Familiarity with observability tooling such as Prometheus, Grafana, and centralized logging systems
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Experience with hardware provisioning, firmware management, and bare-metal automation
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Experience running large-scale distributed training or high-throughput inference workloads
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Familiarity with cloud and hybrid infrastructure across AWS, GCP, or Azure