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 Software Engineer at Magic, you will work on core systems or product surfaces that directly determine model capability and user experience.
This role can map onto Pre-training Data, RL Research & Environments, or Product, depending on background and strengths. Across all placements, the expectation is end-to-end ownership: defining problems, implementing solutions, shipping to production, and iterating based on real outcomes.
Magic’s long-context models introduce unique technical challenges — internet-scale data acquisition, long-horizon post-training loops, and product workflows that make complex model behavior understandable and controllable. You will operate close to these constraints, building systems that are both technically rigorous and production-ready.
This role can evolve into deeper specialization in data systems, post-training capability development, or product engineering leadership, depending on strengths and interests.
What you’ll work on
Depending on team placement, you may:
Build and scale large distributed data pipelines for pre-training
Design filtering, mixture, and dataset versioning systems
Develop post-training datasets, evaluation frameworks, and reward pipelines
Run ablations that translate capability goals into measurable improvements
Build end-to-end product surfaces that integrate deeply with the model
Design APIs, backend services, and frontend workflows for AI-first experiences
Improve reliability, observability, and performance of production systems
What we’re looking for
Strong software engineering fundamentals
High ownership and comfort operating in ambiguous problem spaces
Experience building production systems at scale
Ability to reason clearly about trade-offs between quality, performance, and cost
Strong technical judgment and bias toward shipping
Track record of turning complex technical problems into working systems
Compensation, benefits, and perks (US):
Annual salary range: $200K - $550K
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
Quality. Magic should feel like magic
