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 roleAs 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'll work onOptimize 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
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
Annual salary ranges between $225K - $550K based 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 cultureIntegrity. 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
Quality. Magic should feel like magic
Skills Required
- Strong general software engineering skills
- Thorough knowledge of the deep learning literature
- Experience with pre- and post-training of LLMs
- Experience working with large distributed systems
- Ability to handle large ETL workloads
What We Do
Magic is working on frontier-scale code models to build a coworker, not just a copilot. Come join us: http://magic.dev
