Site Reliability Engineer
Boson AI
On-siteTorontoFull-timeCA$125k–250k/yr
Free daily job alerts: new jobs like this one, by email.
We confirm your address first. Every email has a one-click unsubscribe.
See a sample email
Sample email, with real jobs open today
Your new matches
New jobs that fit the role, pay and location you choose.
Top-paying open roles
Pay as posted by the employer.
- Engineering Manager, Hardware Platform SecurityAnthropic, San Francisco, CA$485k–625k/yr
- Chief Information Security OfficerMETR, Berkeley$493k–579k/yr
- Principal, AI Transformation, CyberOde with Anthropic, San Francisco$450k–600k/yr
Cleared roles
Postings that mention a clearance requirement.
- Cyber Operations SpecialistDelta Solutions & Strategies, Schriever SFB, CO$95k–100k/yr
- Sr. SQL Database Administrator I (6893)MetroStar, Washington, DC$138k–184k/yr
- Network EngineerAccenture Federal Services, Reston, VA$89k–170k/yr
Remote
- MLOps / DevOps Engineer – AI InfrastructureCreatorIQ, Remote$121k–155k/yr
- Senior Associate, Third Party Risk ManagementUpstart, Remote$91k–145k/yr
- Service Desk Level 1 Analyst (Mexico)DYOPATH, Remote
Every job links to the employer's own application page.
Site Reliability Engineer at Boson AI, Toronto (On-site). Pay posted by the employer: CA$125k–250k/yr. Posted Jul 14, 2026, last seen on the employer's hiring page Nov 5, 2026. Mentioned in the posting: AWS, Azure, GCP, Kubernetes.
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...
Show full description
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.
Hiring for this role? Feature it
Source: Boson AI on Lever. FortHire is not the employer and doesn't take applications. Report a problem or request removal.