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Machine Learning Engineer
hace 2 meses
NDT Global GmbH is a leading provider of advanced diagnostic inspection solutions, data analysis, and integrity assessment services for ensuring the safety and longevity of energy-sector infrastructure assets.
Job DescriptionWe are seeking a highly skilled Machine Learning Engineer to join our team and contribute to the development of innovative solutions based on machine learning, especially deep learning, to improve accuracy and efficiency of data analysis.
Key Responsibilities:- Apply supervised techniques, mostly deep learning techniques, for solving pattern recognition problems in ultrasound data (images, signals).
- Understand and interpret data to come up with optimal feature sets.
- Prepare datasets to train and validate models.
- Ensure quality of the developed prototypes by software testing and code reviews.
- Write documentation of developed solutions and ensure knowledge transfer.
- University degree in Machine Learning or similar; alternatively relevant work experience (3 years plus) in machine learning, deep learning or similar.
- Knowledge and experience in Pytorch, Tensorflow, Keras, or Jax is mandatory.
- Python knowledge is mandatory.
- Professional software development experience is preferred.
- Spark knowledge is preferred.
- Experience with git, git workflows, or contribution on GitHub is preferred.
- Knowledge about standard python data science stack is preferred.
- English language skill is a must have.
This is an exciting opportunity to join a fast-growing, innovative industrial technology group focused on advanced diagnostic technologies to monitor the world's infrastructure health.
As a Machine Learning Engineer, you will be part of a team that serves asset owners, large engineering firms, and service companies globally in markets such as Aerospace, Civil Infrastructures, Energy, Mining, Power Generation, and Rail.
Our company is committed to advancing science and technology via sensors, hardware, robotics, and software to make the world more productive and safer for people, business, and nature.
