Machine Learning & Cloud Infra Engineer

About the role

SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and simulation. We are looking for a Machine Learning & Cloud Infra Engineer to build and own the infrastructure that powers our research and productisation of massive foundation models.

Key responsibilities

• Design, provision, and operate multi‑node GPU clusters on cloud and/or on‑prem, handling scheduling, quotas and capacity planning.
• Enable distributed training stacks (e.g., PyTorch DDP/FSDP, NCCL) and ensure performance, stability and reproducibility.
• Build and optimise storage and networking for petabyte‑scale datasets, including object storage, NVMe and caching.
• Package and deploy workloads with Docker and Kubernetes, maintain infrastructure‑as‑code (Terraform) and reliable release pipelines.
• Implement observability, monitoring, logging and alerting; define SLOs and manage on‑call/incident response.

Required profile

• 3+ years of professional experience in infrastructure, platform or cloud engineering (ML‑infra experience strongly preferred).
• Hands‑on experience with GPU compute, performance debugging and networking bottlenecks.
• Strong experience operating cloud environments (AWS, GCP or Azure) including IAM and cost management.
• Proficiency with containers, orchestration (Docker, Kubernetes) and infrastructure‑as‑code (Terraform).

Required skills

• Python, Bash/PowerShell scripting.
• CUDA, NCCL, GPU utilization profiling.
• AWS, GCP or Azure cloud services.
• Docker, Kubernetes, Terraform.
• PyTorch, DDP/FSDP distributed training.
• Prometheus, Grafana, OpenTelemetry, ELK stack.
• CI/CD tools such as CircleCI or GitHub Actions.