You are a seasoned software engineer with experience in machine learning. In particular, you have worked in a production environment, implemented and deployed machine learning and/or data pipelines. You will work closely with data scientists and data engineers in order to integrate and optimize machine learning pipelines.
MLE's assist data scientists in creating production ready pipelines. Beside yielding high functional accuracy, our machine learning pipelines must be scalable and optimized wrt CPU and memory footprint.
MLE's assist data engineers in integrating the machine learning pipelines on our production platform in an efficient and scalable way.
MLE’s bridge the gaps between data scientists and data engineers. They need to speak the language of both. They bring and consolidate new engineering knowledge into the data science team.
You will work on state-of-the-art methods for both supervised and unsupervised learning, including the latest deep learning algorithms.
You will work with big data (GPS fixes and sensors) from hundreds of thousands of users.
You will optimize and deploy training and inference pipelines for predictions of low events/concepts (type of transports, home/work location) and human behavior (commuting, sporting, shopping).
You will create tools to improve development of machine learning pipelines.
You will help the team to improve upon current methods and models, both from a performance and architecture point of view. With a practical mindset you will bring these models into a production environment.
Your software development experience will allow you to closely collaborate with our Data Engineering team to improve our models and data flows and push them through our release process.
You will work in one of our office in Antwerp (Belgium), Vilnius (Lithuania), or Toronto (Canada).
Desired skills and expertise
You have a Masters degree in computer science or a related field.
You have experience in software engineering and deployment to production.
You have experience with Python and Java.
You have implemented and deployed a variety of machine learning pipelines in a production environment.
You have a deep understanding of machine learning concepts such as cross-validation, clustering, supervised vs unsupervised learning.
You can use big data tools such as Spark for development.
You can work independently and take matters into your own hands.
You have the Ability to critically interpret output quality based on quantitative analysis and tooling.
Experience in processing of sensor data from mobile devices or wearables.
Data engineering expertise.
Knowledge of stream processing (Kafka).
Knowledge of mobile development constraints and peculiarities.
What we offer
At Sentiance people come to have an impact and learn. You’ll be a part of an international team brought together by a culture of technical excellence, grit and integrity. You’ll find our compensation and rewards competitive and of course, we have all the start up essentials: free coffee, snacks, flexibility. Better yet, expect an agile and flat structure, dynamic growth opportunities, and an openness for the curious.