Member of Technical Staff, Supercomputing Platform & Infrastructure

Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pre-training, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.

As an engineer on the Supercomputing Platform & Infrastructure team, you will design, build, and operate the large-scale GPU infrastructure that powers Magic’s model training and inference workloads.

A core part of this role is building and maintaining our infrastructure using Terraform-driven infrastructure-as-code practices, ensuring reproducibility, reliability, and operational clarity across clusters spanning thousands of GPUs.

Magic’s long-context models create sustained pressure on compute, networking, and storage systems. Long-running distributed jobs, high-throughput data movement, and strict availability requirements demand infrastructure that is automated, observable, and resilient by design. You will own the systems and IaC foundations that make this possible, including the Kubernetes (K8s) environments that coordinate workloads across our GPU infrastructure.

This role can evolve into broader ownership of supercomputing platform architecture, shaping how Magic scales GPU clusters and infrastructure reliability as model workloads grow.

• Design and operate large-scale GPU clusters for training and inference

• Build and maintain infrastructure using Terraform across cloud and hybrid environments

• Deploy, operate, and optimize K8s clusters used to schedule and manage AI workloads

• Develop modular, scalable IaC patterns for compute, networking, and storage provisioning

• Improve deployment reproducibility, environment consistency, and operational safety

• Optimize networking and storage systems for high-throughput AI workloads

• Automate fault detection and recovery across distributed clusters

• Debug complex cross-layer issues spanning hardware, drivers, networking, storage, OS, and cloud

• Improve observability, monitoring, and reliability of core platform systems

• Strong systems engineering fundamentals

• Deep, hands-on experience with Terraform, including module design, state management, environment isolation, and large-scale deployments

• Experience operating production GPU infrastructure or high-performance distributed systems

• Strong understanding of networking and storage systems

• Experience with major cloud platforms (GCP, AWS, Azure, OCI, etc.)

• Track record of owning production-critical infrastructure end-to-end

• Integrity. Words and actions should be aligned

• Hands-on. At Magic, everyone is building

• Teamwork. We move as one team, not N individuals

• Focus. Safely deploy AGI. Everything else is noise

• Quality. Magic should feel like magic