Site Reliability Engineer (AI Infrastructure)

Location: London, United Kingdom. Hybrid working model with in-person collaboration in the office and remote work.

Company

hirify.global is building an AI platform for autonomous driving that enables vehicles to learn from real-world experience and adapt across different environments and vehicle platforms.

What you will do

• Build and operate platforms providing large-scale GPU compute, Kubernetes, storage, and deployment capabilities for AI development.
• Identify reliability risks and turn recurring operational issues into durable engineering solutions.
• Improve observability, automation, deployment safety, capacity planning, incident learning, SLOs, and production-readiness practices.
• Develop GPU node health detection and automated recovery, resilient distributed AI training, and continuous GitOps delivery with Argo CD or Flux.
• Enhance Kubernetes scheduling, autoscaling, policy enforcement, resource efficiency, and forecasting for GPU and storage capacity.
• Create reusable self-service capabilities and troubleshoot failures across compute, networking, storage, and distributed workloads.

Requirements

• Hands-on experience owning the reliability of large-scale cloud, Kubernetes, or distributed infrastructure.
• Recent experience coding, automating, configuring, or troubleshooting production systems.
• Proficiency in Python or Go, infrastructure as code, CI/CD or GitOps, and modern observability practices.
• Understanding of distributed-systems failure modes across compute, networking, and storage.
• Evidence of measurable improvements to reliability, performance, capacity, or cost, along with effective cross-team collaboration.
• Ability to work in a hybrid setup in London, United Kingdom.

Nice to have

• Direct experience with GPU, ML-training, or HPC infrastructure.
• Experience solving comparable large-scale platform challenges.

Culture & Benefits

• Hybrid working with core hours and opportunities to work hands-on in vehicle workshops and labs.
• Relocation support and visa sponsorship where applicable.
• Market-benchmarked salaries, meaningful equity, and location-dependent benefits.
• Learning and development budgets for training, conferences, and professional growth.
• Health insurance, dental coverage, enhanced parental leave, retirement or pension benefits where applicable, therapy access, wellbeing partnerships, and team socials.

Hiring process

• Initial recruiter call or screen.
• Competency interviews followed by deep-dive technical interviews.
• Final interview focused on mission and values alignment.