Citadel AI builds next-generation machine learning infrastructure, to make the world's AI systems more reliable and secure.
Our company is located in Tokyo, but we have an international team and global target market. Our primary language is English.
As a founding engineer, you'll work side-by-side with our CTO, Kenny Song, to solve problems in machine learning reliability. Kenny was previously a product manager at Google Brain, and was responsible for developing Google's ML infrastructure (TensorFlow, TFX, AutoML).
Since you'll be one of Citadel AI's first engineers, you'll have significant impact and ownership over the future of our company. We're supported by FoundX and 東大IPC, and can share funding details privately.
We're hiring engineers to work side-by-side with our CTO (Kenny) to develop our suite of products for machine learning reliability. Our first product is Citadel Radar, which provides data and model monitoring.
Specifically, we're hiring for ML, backend, or frontend. Past engineering projects include:
In this job, you'll learn about the frontiers and limitations of machine learning, and think about how we can fix them. You don't need to know everything – if you join, we'll teach you what you need to know. If you are already an ML expert, you may read papers in adversarial ML, XAI, and MLSys. We hope that's exciting!
We're a small, agile team, so if your focus is backend, you'll also learn about and make contributions to the ML and frontend. As a founding engineer, you'll also help define our products, establish our engineering principles, and grow our company.
Some technologies we use: Docker, Gunicorn, Redis, TensorFlow Extended, D3.js, Chart.js, and many Python ML libraries.
Compensation will be a negotiable combination of salary and equity.
Our company, market, and technology are new and growing rapidly, so you'll often spend time learning new things. As a result, we value the ability to learn fast over pre-existing knowledge.
You should have software engineering experience in backend, frontend, or machine learning. In addition to your core focus, you should be open to learning and moving across the stack when necessary.
Nice-to-have (not required): you've worked at a startup before, or you have experience with production machine learning.
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