Learn more about Semiotic Labs here
What you will do
As a Software Engineer Machine Learning Operations (MLOps), your objective is to design, build and run our machine learning stack in such a way that we can easily scale up our analysis to many thousands of sensors/electric motors. Your key responsibilities are:
- Move newly finished research of the data science team to production
- Improve the stability and the reliability of our current pipeline
You will be working on daily basis with both data scientists and developers. It will be your task to review code from data science in the research repository that has already been approved by data science. This means that we already know that the functionality is useful and executes the correct logic. Now, you will have to think about how to schedule the code and put it into production. Also, you must think about the computational efficiency and robustness, meanign that where possible (in these areas) you make improvements to the existing code.
You will be monitoring the data flow, databases and other systems related to our data science pipeline, where you are again focused on robustness and efficiency. It is considered a big plus if you have (some) knowledge about machine learning, allowing you to understand what determines the accuracy of the data and our models. For us at Semiotic Labs, it is not only important that our algorithms run, but simulatenously process the right data and have a high accuracy - ultimately improving our reliability.
You will be part of the Software Engineering team, taking care of the various software engineering challenges associated with data science and machine learning.
How we work
The development team uses its own simple, practical, and functional methodology, allowing you to fully focus on software engineering without having to switch tasks. You will work based on the principle of freedom and responsibility.
We take pride in combining professionalism, trust, and very few meetings.
The development team
The software engineering team currently consists of 9 experienced developers working on a large architecture covering a wide domain, from signal processing on our edge devices through dashboarding for different user groups. Continuous improvement and daily deployments are at the heart of the high level of quality we deliver.
Curious to learn about what it's like to join our team of developers?
Tech stack
Since we work on cutting-edge technology, we use an up-to-date stack:
- Python to orchestrate our infrastructure
- AWS - we’re using a broad range of AWS services (20+).
- Terraform
- The team prefers to use Linux distributions for their development environments