Platform Engineer

AI Platform / ML Infrastructure Engineers

• Kubernetes-based compute platforms
• GPU scheduling, batch & distributed workloads
• Supporting ML training, inference, and experimentation at scale

HPC / GPU Engineers

• Job schedulers, MPI, multi-node workloads
• Hybrid cloud and on-prem compute
• Performance, reliability, and cost optimisation

Strong Data Engineers

• Large-scale data pipelines and data platforms
• Data reliability, orchestration, and observability
• Close collaboration with ML and research teams

What You'll Work On

• Designing and evolving Kubernetes-based compute platforms across hybrid and multi-cloud environments
• Building and operating GPU-enabled infrastructure for ML and scientific workloads
• Developing and maintaining core platform services, APIs, and internal tooling
• Improving CI/CD pipelines and Infrastructure-as-Code workflows
• Implementing monitoring, alerting, and reliability engineering practices
• Ensuring security, data protection, backup, and disaster recovery best practices
• Partnering closely with ML engineers, data scientists, and researchers to unblock compute and data challenges

• Strong experience in one or more of:
• Platform / infrastructure engineering
• ML infrastructure or MLOps
• HPC or GPU compute
• Data engineering at scale
• Solid experience with Linux and cloud environments
• Hands-on work with Kubernetes or distributed systems
• Experience with Python (or similar) for automation or services
• Familiarity with CI/CD, Git-based workflows, and automation
• Strong problem-solving skills and a collaborative mindset

Bonus

• Terraform or other IaC tools
• Slurm, Kueue, Ray, Spark, or similar systems
• GPU tooling (CUDA, Nvidia operators, schedulers)
• Experience supporting ML training or data science teams