Requirements:
- MS/PhD degree in computer science or related field;
- Deep knowledge and proven practical experience in a relevant field of research, such as machine learning, computer vision;
- Solid Experience architecting and developing AI and machine learning applications;
- Strong understanding of machine learning algorithms and deep networks;
- Experience with some of the well-known neural networks architectures such as Yolo/SSD, MobileNet, U-Net, Hourglass, RetinaNet, R-CNN-based architectures;
- Strong knowledge in machine learning fundamentals i.e. regression models, decision trees, naïve Bayes, clustering algorithms (k-means, DBSCAN, SOM), dimensionality reduction (PCA, t-SNE) and a good grasp of the strengths and weaknesses of specific approaches. A good foundation in basic statistics and linear algebra;
- Strong knowledge in classical computer vision fundamentals i.e. OpticalFlow, HOG, feature detection algorithms, Hough Transform, Homography, Morphology, Denoising/Deblurring, and image processing algorithms;
- Strong Python knowledge;
- Strong practical experience with DL/CV frameworks like OpenCV, PyTorch, Tensorflow, MXNet or Keras;
- Comprehensive knowledge of the Python data analyses ecosystem (Pandas, Numpy, Scikit-learn, etc.);
- At least minor experience with python visualisation tools (matplotlib/seaborn, Plotly);
- Understanding state-of-the-art CV approaches for problems like object detection/tracking, video analysis, semantic segmentation, pose estimation, optical character recognition;
- Experience with lightweight web-frameworks for DL methods exposing, like Flask and Dash;
- Upper-intermediate level of English mandatory.
Would be a plus:
- Experience with C++;
- Experience with the following modern neural network architectures: LSTM and other RNN-based, Transformers(BERT, etc.);
- Familiarity with time-series predictive/anomaly detection analyses, natural language processing, signal processing;
- Understanding SOTA approaches for machine learning problems like unsupervised / semi-supervised learning;
- Experience with the following frameworks: DLib, Darknet, Theano;
- Awareness of the CRISP-DM process model;
- Experience with continuous integration and release management tools, preferably within the AWS platform;
- Experience with versioning and control system for experiment conduction, like DVC and so on;
- Hands-on Experience with the common architecture of MLOps system by the means of Hadoop, Docker, Kubernetes, cloud services and experience with managing production ML lifecycle.
With us, you can:
Develop your technical knowledge:
- Use the latest technologies;
- Participate in technical events and conferences (the cost is covered by the company);
- Regular techtalks and professional development.
Improve your soft skills:
- Build strong teamwork skills and become an essential part of the dynamic teams;
- Improve your English in classes and speaking directly with clients;
- Increase your productivity and communication level via Scrum, Kanban, Agile methodologies.
What else do we offer?
- Competitive compensation and benefits;
- Flexible and negotiable schedule;
- Nice and comfortable office;
- Covered rest period (20 business days);
- Free English classes (we have 4 teachers in our team)
- Truly friendly atmosphere