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.

About the role

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.

What you’ll work on

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

What we’re looking for

Strong software engineering skills with experience building production infra systems
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

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Member of Technical Staff, Supercomputing Platform & Infrastructure

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