About Semiotic Labs
Passionate about solving the problem of unplanned downtime, Semiotic Labs uses AI-driven electrical waveform analysis to create smart predictive maintenance solutions that increase productivity while saving customers’ time and money. We serve customers such as Vopak, Schiphol, Nouryon and ArcelorMittal. Our product, SAM4, leverages machine learning algorithms and IoT sensors to detect upcoming failures in critical industrial assets up to months in advance. But SAM4 does more than just prevent unplanned downtime. It also enables the world's largest and most successful industrial companies to significantly reduce energy waste.
We are a growing, energetic company where professionalism, a great atmosphere, and cutting-edge technology go hand in hand. Our team in Leiden, the Netherlands, currently has 50 people and continues to rapidly grow.
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:
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.
Since we work on cutting-edge technology, we use an up-to-date stack:
Who should apply?
As a Software Engineer Machine Learning you have a computer science background, at least 3 years of software development experience, and a good understanding of artificial intelligence/ machine learning and infrastructure. Next to this, you have:
a good understanding of modern databases;
experience with designing and running cloud infrastructure, including distributed systems and clusters;
knowledge of configuration management tools;
experience with modern software development practices (e.g. continuous delivery).
Your favorite stack
It’s a big plus if you have experience with
What we offer
Please note Semiotic Labs does not relocate candidates from outside the EU.
We will only hire people with an EU passport, have eligibility to work in, or are already highly skilled migrants in the Netherlands.