Machine Learning & Cloud Infra Engineer

The role

You will build and own the infrastructure that powers World Model research and productization. You will design, implement, and operate scalable training and data systems for large diffusion-based generative models.

What you will do

• Own and evolve the ML + cloud infrastructure for training and evaluation of massive foundation models.
• Design and operate GPU clusters including provisioning, scaling, and maintaining multi-node environments.
• Support high-throughput distributed training stacks.
• Build and optimize storage systems and networking for petabyte-scale datasets.
• Package and deploy workloads with Docker and Kubernetes.
• Maintain infrastructure-as-code using Terraform.
• Implement monitoring, logging, and alerting for cluster health and job performance.
• Manage secrets, IAM, and secure network boundaries.
• Partner with ML researchers and engineers to improve developer experience.
• Support model evaluation and serving infrastructure.

Required qualifications

• 3+ years of professional experience in infrastructure, platform, or cloud engineering
• Hands-on experience with GPU compute and performance debugging
• Strong experience operating cloud environments (AWS, GCP, or Azure)
• Proficiency with containers and orchestration (Docker, Kubernetes)
• Infrastructure-as-code (Terraform)
• Strong scripting and automation skills (Python plus Bash/PowerShell)
• Familiarity with distributed training and modern ML stacks (PyTorch; DDP/FSDP or comparable)
• Experience with monitoring and observability tooling (Prometheus/Grafana, OpenTelemetry, ELK, or similar)
• Experience building CI/CD for infra and ML workflows (e.g., CircleCI, GitHub Actions)

Nice to have

• ML infrastructure experience

About the company

SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and simulation.