Boson Ai

Develops scalable AI tools for enterprises

Site Reliability Engineer

Site Reliability EngineeringConfirmed live in the last 24 hours

Full-timeToronto, ON, CanadaCA$125k - CA$250k /yr

About the Job

Requirements

At least 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, Autonomous System Number and Border Gateway Protocol 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; or 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.

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 machine learning, 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.

Desired Qualifications

Experience supporting GPU-intensive AI or high-performance computing 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.

Salary Range: 125000 to 250000 (Currency: CAD) (Pay period: per-year-salary)

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.

Boson AI uses artificial intelligence (AI) to help screen and assess applications for this position, and our team reviews the shortlisted candidates.

About the Company

Boson Ai

Develops scalable AI tools for enterprises

Boson AI develops large language model tools to power AI-driven experiences in virtual worlds. Its products understand and generate human-like text and are designed for wide use, from individuals to large enterprises. The tools work by combining advanced deep learning with system engineering to create customizable LLM-based applications that can be embedded into various software to personalize storytelling, learning, content creation, and data insights. Boson AI differentiates itself by focusing on tailored experiences in virtual environments across multiple industries, offering scalable solutions through product sales, subscriptions, and licensing. The company’s goal is to provide practical, personalized AI tools that enhance user interactions, storytelling, education, and business intelligence in virtual settings, becoming a leading provider in the AI market.

Company size: 11-50
Company stage: Not listed
Total funding: Not listed
Headquarters: Santa Clara, CA
Founded: 2023

Recently Posted Jobs

Explore more opportunities at Boson Ai

See more jobs