Site Reliability Engineer

About The Role

Boson AI builds production-grade AI systems that make communication with AI more natural, capable, and useful. We are looking for a Site Reliability Engineer to help build and operate the infrastructure behind that work.

Based 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

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.