Research Engineer

Magic in San Francisco

Join us to build and safely deploy aligned, superhuman AI. For decades, technology was "just a tool" - could it be a colleague?

For decades, technology was “just a tool.” Soon, it will be a partner. We are building an AI pair programmer that feels like a full colleague inside your computer - capable, conversational, and reliable across domains. If this isn't AGI, then what is? Join us if you want to build and safely deploy aligned AGI in products that matter.

As a Research Engineer, you’ll work on training, evaluating, and serving large AI models, internet-scale dataset building, and help prototype new research and product ideas.
About Magic
Magic is a public benefit corporation dedicated to building and safely deploying aligned, superhuman AGI.

FAQ:
What's your motivation?
Automation has led humanity from subsistence farming to becoming a globally connected society. AGI is the ultimate chapter of the story of human tool-building, presenting the potential to decouple productivity and ingenuity from human labor. What if the last 50 years of technological progress happened in 2 days? We want to make this a possibility.

Funding?
We've recently raised $28M.

How do we balance deploying the technology today with ambitions for AGI?
We think deploying AI within the right interfaces is just as important as the technology itself. Building an AI pair programmer helps us do both at the same time. We aim to launch gradually improving AI assistants while pursuing work on what will ultimately become AGI.

Do you train your own models?
Yes

Do you care about the product?
It's funny that this is an FAQ, but many AI companies neglect UX and focus only on their model. Yes, we care.

Can I work from anywhere?
We welcome applications from anyone around the world. We'll look at visa requirements case by case.

I don't meet all the criteria, should I still apply?
If you feel you have something to contribute to the mission and you're a high-energy person, absolutely. We make exceptions for exceptional people.
    • Pre-training: Distributed training across large GPU clusters
    • Inference: Optimizing inference throughput of a custom neural net architecture
    • RL: Continual learning to train AI models on user feedback
    • Tool use: Implement interfaces to allow our AI models to interact with real-world tools or APIs
    • Algorithms: Designing, testing, and optimizing new neural net architectures
    • Internet-scale data: E.g. scraping much of YouTube
    • Integrity. Words and actions should be aligned.
    • Hands-on. Most of us have previously led engineering teams. At Magic, there are no managers. We all spend the vast majority of our time on engineering. If you want to solve hard problems, Magic is the right place for you.
    • Teamwork. We move as one team, not N individuals.
    • Focus. Ethically deploy AGI. Everything else is noise.
    • Quality. We have high standards for ourselves and our products. Magic should feel like magic.
    • Benchmark-based compensation in the 75th or 90th percentile, including base salary, equity, and benefits
    • Flexible working hours
    • In-person (London, SF, NYC, or Vienna) or remote
    • A small, fast-paced, highly focused team
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