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Data Systems Engineer
You care deeply about reproducible, scalable pipelines and clean data sets. You can write a program in SQL (not that you'd want to). You can refactor a ETL project in 2 days if provided with enough liquid motivation. You can take a pipeline that is behaving erratically and make it produce the same result every time. You understand how to talk to and interact with machine learning researchers about your pipelines and tools and can teach them how to use them. You will build systems that move data from A to B reliably, under all sorts of constraints and heavy fire.
Must
Desirable
At Aiconic we move, merge and build machine learning systems using billions of rows and Terabytes of data reliably and reproducibly. We work on hard problems where the right choice matters, as people rely on our algorithms to make their most important decisions. We care more about the ability to learn fast than experience with certain libraries or systems. Our engineering culture values the academic method and good code.
We offer you to work with interesting data on problems that matter with interesting and diligent coworkers and the opportunity to shape the future slowly but surely.
We look for a track record of exceptionalism - hackathon winners, competitive schools, published papers, Kaggle masters or extraordinary projects.
Process:
We build end-to-end systems for top tier companies that create material impacts. This includes data pipelines and database infrastructure, development of state-of-the-art machine learning systems, and real-time prediction APIs.
Our diverse team graduated from some of the leading universities in the world, such as NYU, UC Berkeley, and the University of Amsterdam. We are researchers and engineers with a wide range…
We build end-to-end systems for top tier companies that create material impacts. This includes data pipelines and database infrastructure, development of state-of-the-art machine learning systems, and real-time prediction APIs.
Our diverse team graduated from some of the leading universities in the world, such as NYU, UC Berkeley, and the University of Amsterdam. We are researchers and engineers with a wide range of quantitative backgrounds, capable of covering entire project verticals. We actively publish at top machine learning conferences like ICML, AISTATS, NeurIPS, and UAI.
We have a cross-european perspective, both in our project selection and recruiting efforts.
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