TL;DR

Staff Software Engineer (GPU Infrastructure HPC): Build and scale Kubernetes-based GPU/TPU superclusters for AI/ML workloads with an accent on high throughput, low latency, and cost efficiency. Focus on optimizing HPC infrastructure, troubleshooting complex issues, and enabling AI researchers with self-service tools.

Location: Hybrid in Canada and United States

Company

%hirify_global% develops frontier AI models and infrastructure to power advanced AI systems for developers and enterprises.

What you will do

Build and scale ML-optimized HPC infrastructure across multiple clouds using Kubernetes.
Optimize infrastructure for AI/ML training focusing on cost efficiency, reliability, and performance.
Troubleshoot and resolve complex infrastructure issues to minimize disruption.
Design self-service tools for researchers to monitor and optimize training jobs.
Collaborate with AI researchers to innovate and translate emerging needs into scalable infrastructure.
Promote best practices in observability, automation, and infrastructure-as-code.

Requirements

Location: Must be able to work hybrid in Canada or United States
Deep expertise in GPU/TPU clusters, distributed training frameworks (JAX, PyTorch, TensorFlow), and HPC environments.
Proven experience managing Kubernetes clusters at scale for AI workloads.
Strong programming skills in Python and Go with open-source contributions preferred.
Knowledge of Linux internals, RDMA networking, and performance optimization for ML workloads.
Experience collaborating with AI researchers or ML engineers.

Culture & Benefits

Inclusive and open culture with a focus on diversity.
Work closely with cutting-edge AI research teams.
Weekly lunch stipend, in-office lunches, and snacks.
Full health and dental benefits including mental health budget.
100% parental leave top-up for up to 6 months.
Personal enrichment benefits and remote-flexible work with offices in multiple global cities.
Six weeks of vacation (30 working days).