Magic.dev

As a Research Engineer on the Pre-training Systems team, you will design and operate the distributed infrastructure that trains Magic’s long-context models at scale. This role focuses on large-scale model training across massive GPU clusters, working at the boundary between deep learning and distributed systems to ensure training runs are performant, reliable, and reproducible under extreme scale. You will own the systems that make large-scale pre-training stable and fast, addressing challenges like sustained memory pressure, communication overhead across thousands of devices, long-running jobs that must survive failures, and efficient sequence packing under hardware constraints.

$275k - $550k

As a Research Engineer on the Inference & RL Systems team, you will design and operate the distributed systems that serve our models in production and power large-scale post-training workflows. This role sits at the boundary between model execution and distributed infrastructure, focusing on systems that determine inference latency, throughput, stability, and the reliability of RL and post-training training loops. You will own the infrastructure that makes both production inference and large-scale RL iteration fast and reliable, addressing demanding execution constraints introduced by Magic’s long-context models.

$275k - $550k

As a Research Engineer on the RL Research & Environments team, you will design and operate the data, evaluation, and environment systems that improve model capabilities after pre-training. This role focuses on post-training: identifying capability gaps, building targeted datasets, designing reward signals, and running iterative training loops that measurably improve user-facing behavior. You will own the infrastructure and experimental workflows that connect product priorities to concrete capability gains. Magic’s long-context models introduce distinct post-training challenges, and you will build systems that expose failure modes, generate high-signal training data, and enable rapid RL iteration at scale. This role can evolve into ownership of major capability areas, deeper RL systems work, or broader influence over post-training strategy.

$275k - $550k

As a Research Engineer at Magic, you will be instrumental in training, evaluating, and serving large AI models, including exploring new inference-time compute techniques. You will also be responsible for building internet-scale datasets and prototyping innovative research and product concepts. This role offers the opportunity to contribute to cutting-edge AI research and development in a fast-paced, highly focused team environment.

$275k - $550k

Magic is seeking a Kernel Engineer to design, implement, and maintain high-performance kernels that optimize throughput and latency for AI model training and inference. This role is crucial for supporting Magic's long-context windows, which present unique challenges in memory utilization, data movement, and sustained throughput. You will be responsible for the entire lifecycle of kernel development, from design and implementation to deployment and ensuring production reliability.

$275k - $550k