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Member of Technical Staff, Supercomputing Platform & Infrastructure

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Magic AI, Inc
Full Time position
Listed on 2026-07-31
Job specializations:
  • IT/Tech
    Systems Engineer, Cloud Computing: Infrastructure & Operations, SRE/Site Reliability, IT Infrastructure
Salary/Wage Range or Industry Benchmark: 200000 USD Yearly USD 200000.00 YEAR
Job Description & How to Apply Below

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.

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

Compensation, benefits, and perks (US):

Annual salary range between $200K - $550K depending on experience

Equity is a significant part of total compensation, in addition to salary

401(k) plan with 6% salary matching

Generous health, dental and vision insurance for you and your dependents

Unlimited paid time off

Visa sponsorship and relocation stipend to bring you to SF, if possible

A small, fast-paced, highly focused team

Magic strives to be the place where high-potential individuals can do their best work. We value quick learning and grit just as much as skill and experience.

Our culture

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

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