About The RoleBased in Toronto or remote, you will work across the systems that enable large-scale AI training and serving: high-performance networks, GPU clusters, storage, scheduling, and the operational tooling that keeps them reliable. This is a hands-on role for someone who enjoys taking complex infrastructure from "it works" to dependable, observable, and scalable.
You do not need to be an expert in every layer of the stack. We are looking for deep strength in at least one area-networking, cluster scheduling, storage, GPU systems, or AI infrastructure- and the curiosity and judgment to collaborate across the rest.
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
$125,000 - $250,000 a year
Boson AI is building AI systems for real-world, business-critical use. If you enjoy solving difficult infrastructure problems and want your work to directly enable the next generation of AI products, we'd love to hear from you.
