Research Engineer
- Pay
- $275,000 – $550,000 per year
- Location
- San Francisco, CA · On-site
- Job type
- Full-time
- Level
- Mid-level
- Listed
- 22 days ago
Skills & highlights for this role
- deep learning
- LLM pre-training and post-training
- distributed systems
- ETL pipelines
Also useful
- reinforcement learning
- large-scale GPU training
Stated benefits & perks
- Health
- Dental
- Vision
- 401k match
- Unlimited pto
About the role
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'll 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
Compensation, Benefits, and Perks (Us)
- Annual salary ranges between $275K - $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
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
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.
From Magic's careers page, first listed on Rolev.ai on October 17, 2026. Rolev.ai is not the employer.
How Rolev.ai helps with this application
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Common questions
What is the salary for the Research Engineer role at Magic?
The Research Engineer posting at Magic states compensation of $275,000 – $550,000 per year. It is listed as a full-time position. Final pay may depend on location, experience, and interview evaluation on Magic's official career site.
Where is the Research Engineer job at Magic located?
This Research Engineer position at Magic is located in San Francisco, CA (On-site). Always check Magic's application page for specific state eligibility or relocation policies.
What qualifications are required for Research Engineer at Magic?
For the Research Engineer role, Magic highlights experience with deep learning, LLM pre-training and post-training, distributed systems, ETL pipelines. Rolev.ai can score your existing resume against these requirements and show any skill gaps before you apply.
How do I apply for the Research Engineer position at Magic?
This listing links directly to Magic's Ashby application page. You can apply directly on Magic's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the Ashby application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.