POSTED Nov 5

Research Engineer

at Magic AI ⋅ United States

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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 a research engineer, you’ll work on training, evaluating, and serving large AI models and new inference-time compute techniques, build internet-scale datasets, and help prototype new research and product ideas.

What you might work on:

Optimize inference throughput for novel model architectures
Contribute to frameworks used for our research and production workloads
Train trillion-parameter models on large GPU clusters
Curate post-training datasets to improve targeted capabilities
Build out internet-scale data pipelines and crawlers
Design, prototype and optimize new model architectures
Contribute to research across long-context, inference-time compute, RL, and more

What we’re looking for:

Strong general software engineering skills
Thorough knowledge of the deep learning literature
Experience with pre- and post-training of LLMs
Ability to come up with and evaluate research ideas
Experience working with large distributed systems
Ability to handle large ETL workloads

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: