As a Machine Learning Engineer, you are partly a computer scientist and partly a research engineer. You have an analytical mindset, a deep understanding of linear algebra and statistics, and provable hands-on programming experience. You will be part of the Applied A.I. team and you are passionate about machine learning, deep learning, and data science in general. You will work on interesting projects for some of the world's most innovative clients and partners as well as work on internal technologies.
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- You have had at least 5 years of work experience and you love a high-growth work environment.
- Your main objective will be to help our customers by building and integrating machine and deep learning models.
- You will design and implement state-of-the-art methods for both supervised and unsupervised machine learning, applied to natural language, spatial, sensor, image, and temporal data.
- You will design and implement state-of-the-art enrichment methods to build data fusion platforms that are able to ingest, acquire, process, and output different kinds of data.
- Your ability to understand and implement state-of-the-art academic research papers will help you to apply novel algorithms to large volumes of real-life data.
- You will help the team to improve upon current methods and models. You have a practical mindset and are able to bring these models into a production environment. As such, you have extensive experience with Python or another relevant programming language.
- Your programming experience will allow you to closely collaborate with back-end developers to improve our models and push them through our release process.
- You have a master’s degree or Ph.D. in computer science, machine learning, artificial intelligence, mathematics, biostatistics, applied statistics, physics, or a related field. Demonstrable equivalent experience is fine too.
- You have provable experience in machine learning, deep learning, predictive modelling, data analysis, or data modeling
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- You possess a deep understanding of supervised and unsupervised learning, clustering, machine learning, regression, optimisation, reinforcement learning, forecasting, and/or recommender systems.
- You have experience with standard software development tools like git, Jira, bash, RegEx, ...
- You have a strong mathematical background and analytical mindset.
- You have a practical mindset and are willing to get your hands dirty. You understand the difference between fundamental research and data driven development.
- You have good knowledge of and experience with the Python stack (pandas, numpy, pytorch, keras, scikit-learn, ...).
- You consider yourself a healthy mix between a machine learning expert, a software engineer, a researcher, and a hacker.
- You are fluent in English.
- You can work independently and take matters into your own hands.
- The ability to quickly learn new technologies and successfully implement them is essential.
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Experience with any of the following is considered a plus:
- Sensor data; AHRS, Kalman filters, particle filters, etc.
- Cloud platforms like Microsoft Azure, Google Cloud Compute, and Amazon AWS.
- Additional programming language skills like Rust, R, or C++ .
- Docker, Kubernetes, TF Serving, continuous integration, microservices architecture, ...
- Experience in the telco, automotive or financial industry.
- Open source contributions.
- Consulting.