Boson AI

Site Reliability Engineer

Rol remoto de Site Reliability Engineering con fit claro de ubicación del candidato.

Publicado19 jul 2026

Países elegibles1 país aceptado

Señal de senioritySenior

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

Canadá

Resumen del rol

Contenido del rol extraído en secciones para revisar más rápido.

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

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

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

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