Who are we looking for?
We are looking for a Senior MLOps Engineer to join our team.
On a daily basis, you’ll work with Senior Machine Learning Engineers, a Machine Learning Architect, and a client based in the U.S. About the project: We’re applying our know-how and experience from medical projects to image data from a completely different industry—a major challenge, but also an incredibly interesting project in which we’ve already achieved significant success! :)
Technology stack
- AI,
- ML AWS,
- Python
- TensorFlow/Keras, NumPy,
- SciPy, scikit-learn
- Pandas
- Jupyter Notebook
- Nice to have: PyTorch, MLFlow, ITK/VTK/PyVista, OpenCV
What will You find here?
- fascinating projects—you can have a real impact on people’s health and lives,
- high technical standards in our projects—you’ll really spread your wings here,
- great people with a proactive approach to work,
- events and team-building activities—we enjoy spending time together😊,
- a professional development budget,
- group insurance,
- medicover health insurance,
- hybrid work arrangements and flexible hours,
- the opportunity to collaborate with the scientific community on an ongoing basis and publish in scientific journals
About the Company:
At Graylight Imaging, we develop technologies for the medical field, with a particular focus on analysing medical imaging studies. In addition to commercial projects, we conduct our own R&D, building machine learning algorithms for cardiology. As a member of our team, you’ll contribute to solving problems that no one has solved before, with the goal of tangibly improving people’s health and lives.
What are we looking for in a candidate?
- a technical college degree (a PhD in computer science, statistics, or mathematics is a plus),
- at least 3 years of experience in the field of ML,
- experience working with AWS (AWS Batch, CloudWatch, SageMaker, S3, etc.)
- experience in performing MLOps tasks (knowledge of Amazon SageMaker, MLFlow, Weights & Biases), creating experiment hierarchies, enabling tracking of parameters and repositories, and building pipelines for preprocessing, training, and inference.
- optimising pipelines by offloading operations from the CPU to the GPU, and developing computational methods such as signal filtering and analysis for the time and frequency domains.
- very good knowledge of data visualisation, exploration, and analysis techniques (especially for numerical and image data, particularly 3D),
- knowledge of statistics, algebra, and mathematical analysis (statistical tests, vector and matrix calculus)
- knowledge of machine learning (particularly deep learning),
- very good knowledge of neural network architectures, including CNN and UNet,
- good knowledge of the Python programming language,
- very good knowledge of image processing and analysis algorithms,
- the ability to efficiently implement and verify methods described in the literature, particularly those related to machine learning,
- an understanding of the advantages and disadvantages of machine learning-based algorithms, as well as the ability to present developed solutions in a clear and comprehensive manner,
- independence, proactivity, openness to change, communication skills, analytical thinking,
- the ability to organize work and manage time effectively,
- very good knowledge of English—at least C1.