Senior Machine Learning Research Engineer (Computer Vision)

12 февраля 2019    32
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— 3+ years of work experience (industry or academia) in the field of computer vision;
— Solid understanding of machine learning algorithms, you know which one to choose for which problem;
— You know how to design experiments and how to statistically analyze the results;
— Experience with a common deep learning framework, preferably pytorch/tensorflow;
— Experience in one or more of the following: CNNs (2D and 3D), Semantic Segmentation, Bayesian Modeling, — Class Activation Mapping (or similar visualization techniques);
— Strong coding skills in Python;
— Strong Upper-Intermediate level of English.

— Experience in medical imaging;
— Experience (C/C++);
— Experience with docker;
— Experience with cloud computing (e.g. on AWS);
— Experience with relational and non-relational data bases;
— Knowledge of a Quality Management System.

— You will have the opportunity to work on impactful, interesting and complex problems beyond standard deep learning object classification;
— You will join a highly interdisciplinary team of motivated and highly collaborative data scientists, engineers, physicists and medical doctors with rapid decision-making processes;
— You will play a key role in shaping our computer vision infrastructure from the ground up;
— Flexible working schedule;
— 8-hour working day (From the office or remotely);
— 22 days of paid vacation allowance a year+sick leaves;
— Unlimited tea/coffee/cookies;

— You will develop and train models for detecting and diagnosing medical conditions from radiological image data (MRI, CT);
— You will be researcher and engineer at the same time: You will scan publications and evaluate the latest research, you will implement, test and analyze the most promising approaches, and finally you will transfer your results to production with the help of our engineers;
— When developing solutions, you have the needs of our stakeholders in mind: In healthcare, interpretability and confidence assessment of predictions are key aspects.

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