Open Positions: full stack | data engineering | machine learning

Locations: Amsterdam, Vienna, Berlin

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. ​


    1. Pre-Screening, Resume, Motivation
    2. Machine Learning / Data / Coding Challenge
    3. Interview with founders
    4. Review of something you created
    5. Paid on-site with the team
    6. Offer

Full Stack Engineer

You are not afraid to build, test and iterate quickly and you feel strongly about good code. You have written html and painfully raw javascript before and are familiar with both. You can take whatever code exists and put it behind reliable apis using your favorite web framework in python, ruby, or javascript. You can be process oriented when it is appropriate but can also maintain an understanding of business goals and adjust your work schedule accordingly. You will make decisions on technology and architecture. You will help data engineers and machine learning researchers produce the best possible results.​


  • BS in computer science or extraordinary aptitude as a software engineer
  • The ability to learn quickly and make decisions under constraints
  • Good python skills


  • ​Experience with basic machine learning concepts
  • Kubernetes, SQL databases, AWS + Azure concepts/experience
  • Understanding of programming languages and compilers
  • Soft communication skills over email and in person

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.


  • BS in computer science or extraordinary aptitude as a data engineer or systems engineer
  • Knowledge of distributed systems like hdfs/hadoop, horizontal database scaling
  • Expertise in data storage, movement, and management systems
  • Ability to write a memory efficient program
  • Ability to work with ETL pipelines
  • Strong understanding of SQL
  • Excellent bash/python skills


  • Experience with aws, azure or google cloud
  • MS/Phd in computer science or similar technical field
  • Experience with airflow, luigi or similar ETL pipeline software
  • Experience with legacy database systems (e.g. oracle / cobol db2)
  • Knowledge of operating systems, programming languages, and compilers

Machine Learning Research Engineer

You are a scientist who thinks like an engineer. You care deeply about experimental setup, the scientific method, focus on results and real world impact. You will build machine learning models on billions of rows of data. You will solve problems in the context of business goals with ample time to think about problems and read the latest research in an academic setting. You will attempt to generalize every day problems to global patterns and collaborate on ongoing research efforts.


  • MS in ML, Physics, Mathematics, Statistics, CS or evidence of extraordinary quantitative aptitude
  • Oversight to consider the engineering tools and the product you're algorithm lives in
  • Ability to work with messy real world data and the desire to create real world impact


  • PhD. or published papers at top research conferences
  • Track record of building competitive algorithms (e.g. Kaggle)
  • Thoroughly documented and presentable project case demonstrating aptitude