Magic
Magic builds frontier-scale code models designed to act as an AI coworker for software developers and engineering teams, automating code generation and research tasks. Its products center on developer-facing models and tooling that integrate into software workflows for teams seeking higher velocity and reliability. Founded in 2022 and headquartered in San Francisco, the privately held company focuses on AI-driven developer tools spanning information technology and machine learning.
Open Positions at Magic
6 open positions
Build the software, automation, and infrastructure that defend Magic’s frontier AI systems and engineering organization from traditional and emerging AI cyber threats. This hands-on role combines security engineering, infrastructure protection, secure sandboxing, threat response, and LLM-native attack defense.
Build and operate distributed systems that train long-context models across massive GPU clusters. You’ll improve training performance, reliability, fault tolerance, and reproducibility at frontier scale.
Build and operate the distributed inference and RL systems that make Magic’s long-context models fast, reliable, and scalable. You’ll optimize serving performance, post-training workflows, and production infrastructure across GPU, networking, and storage layers.
Build the datasets, evaluation frameworks, reward signals, and environments that drive reinforcement-learning post-training for long-context models. You’ll connect software, data, infrastructure, and research experiments to measurable capability improvements.
Join Magic in a unique role focused on building safe AGI through innovative research and code generation.
Design and optimize high-performance GPU and accelerator kernels powering long-context AI training and inference. You’ll own kernel development through production while tackling challenging memory, data movement, and throughput problems for frontier AGI systems.