Boson AI
Site Reliability Engineer
TLDR
Design, deploy, and optimize high-performance AI networking and infrastructure (InfiniBand, GPU clusters, Ceph) to power training and inference.
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.
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.
Benefits
Remote-Friendly
Fully remote role
Boson AI creates advanced AI solutions tailored for businesses, focusing on large language models and agentic systems to address complex real-world challenges. Our distinct approach combines innovative research with practical applications, enabling companies to enhance their operations and unlock substantial value.
Site Reliability Engineer