We are actively looking for software engineers based in Munich or remote/hybrid to join our team!
Thank you for your interest! You already should know about us, DQC.ai, and hopefully, you are as interested in changing the future of data (quality) as we are! 😉
Tech stack:
- BackEnd
- Python (Polars, SQLAlchemy, DuckDB, ...)
- CatBoost
- Mixtral/DolphinMixtral
- GPT3.5/4 (fine tuned)
- k8s
- FrontEnd
- React
- TypeScript
We would like to make a dent in the data universe. Making bad data obsolete. And we are looking for people sharing this goal! Because that's a hard probleme, we are also looking for people who like to solve hard problems - obviously.
If you're interested in joining, please provide the following materials.
The ultimate measure of an engineer is our work. Please submit at least one work sample (and no more than three), providing links if/as necessary. This should be work that best reflects you as an engineer -- work that you are proud of or you feel is otherwise representative of who you aspire to be as an engineer. If this work is entirely proprietary, please describe it as fully as you can, providing necessary context.
Writing is an essential part of communication. What’s an example of writing that you are proud of? This writing can take a variety of forms, E.g.:
- A block comment in source code
- A blog entry or other long-form post on a technical issue
- A technical architecture document, design document, specification, whitepaper or academic paper
Please submit at least one writing sample (and no more than three) that you feel represents you, providing links if/as necessary.
Data quality sample: A fundamental aspect of our software is ensuring the integrity and reliability of data within a system. Handling poor data quality is not just a task but an ongoing challenge that can significantly impact the performance and outcomes of projects. Please provide a data quality sample: a detailed account of a time when you encountered and addressed issues stemming from poor data quality.
Analysis sample: A significant challenge of engineering is dealing with a system when it doesn’t, in fact, work correctly. Finding a bug is hard work, and from our experience, the best teams are very good in finding bugs fast, with a low mean time to debug.
Please provide an analysis sample: a written analysis of a data quality issue or bug from some point in your career. If such an analysis is not readily available, please recount an incident, including as much technical detail as you can recall.
Please answer the following questions, using as much space as you need. All of these questions are important, but the final question is probably the most important one; take your time in answering it!
What work have you found most challenging in your career and why?
What work have you done that you were particularly proud of and why?
When have you been happiest in your professional career and why?
When have you been unhappiest in your professional career and why?
What was a recent tasks where you had a creative or magical moment writing code?
Why do you want to work for DQC.ai?
(Credits to Oxide for opening their hiring materials and process!)