Staff Software Engineer (AI Infrastructure)

TL;DR

Staff Software Engineer (AI Infrastructure): Building and scaling compute infrastructure for AI models with an accent on node lifecycle management, automated hardware repair, and cluster orchestration. Focus on optimizing accelerator capacity (GPU/TPU), designing high-availability distributed systems, and scaling infrastructure to hundreds of thousands of hosts.

Location: Hybrid in London, UK (must be in office at least 25% of the time)

Salary: £325,000 - £485,000 GBP

Company

hirify.global is a public benefit corporation dedicated to creating reliable, interpretable, and steerable AI systems for the benefit of society.

What you will do

• Own the technical strategy and roadmap for node lifecycle management, including ingestion, bring-up, health checking, and automated repair.
• Drive cross-team initiatives to scale AI clusters across multiple clouds and accelerator families.
• Design and operate systems that automatically detect and remediate unhealthy hardware to minimize capacity loss.
• Define high-level infrastructure architecture and solve complex technical challenges directly or through other engineers.
• Collaborate with cloud providers and internal research/product teams to shape long-term compute and data strategy.
• Provide technical mentorship and coaching to support the growth of other engineers.

Requirements

• Deep expertise in distributed systems, reliability, and cloud platforms (Kubernetes, IaC, AWS/GCP/Azure).
• Strong proficiency in Rust, Go, or Python and expertise with Terraform.
• Hands-on experience with machine learning accelerators such as GPUs, TPUs, or Trainium.
• Track record of leading complex, multi-quarter technical initiatives spanning multiple teams.
• Must be based in or able to work from the London office at least 25% of the time.
• Bachelor’s degree or equivalent professional experience in a relevant field.

Nice to have

• Experience managing hyperscale compute infrastructure (10K+ nodes).
• Deep knowledge of Kubernetes internals (scheduler, autoscaler, kubelet, Karpenter) or orchestration systems like Borg or Mesos.
• Low-level systems experience with kernel, virtualization, device drivers, or firmware.
• Familiarity with high-performance networking (EFA, RDMA, InfiniBand) for distributed ML workloads.
• Contributions to relevant open-source projects (e.g., Kubernetes, Linux kernel).

Culture & Benefits

• Competitive compensation package including optional equity donation matching.
• Generous vacation and parental leave.
• Flexible working hours and a collaborative office environment.
• Strong focus on AI safety and commitment to team diversity and representation.
• Visa sponsorship available for qualified candidates.