Job Description
Our goal at Liquid is to build the most capable AI systems to solve problems at every scale, such that users can build, access, and control their AI solutions. This is to ensure that AI will get meaningfully, reliably and efficiently integrated at all enterprises. Long term, Liquid will create and deploy frontier-AI-powered solutions that are available to everyone.
Our ML technology is proven and validated. Now comes the engineering challenge: building the systems and infrastructure that turn theoretical capabilities into deployable products. This isn't about maintaining existing systems – it's about architecting the foundation for rapid ML development and deployment at scale.
We're looking for engineers who understand that good infrastructure is the difference between theoretical and practical ML. The challenge isn't just writing code – it's making the right technical decisions that enable speed without sacrificing stability.
"Can you build systems that enable both rapid development and robust deployment of ML models, while resisting the urge to over-engineer?" If you have strong opinions about architecture but know when to be pragmatic, you might be who we're looking for.
Ideal for engineers who've built and scaled systems from zero to production, understand the trade-offs at each stage of growth, and want to apply that knowledge to revolutionize how ML models are developed and deployed.
Liquid AI is a technology company that develops artificial intelligence solutions for various applications. It focuses on creating tools for data analysis and decision-making processes. Liquid AI serves industries such as finance, healthcare, and logistics.
