Computer Vision Engineer

We are a distributed team that develops motion detection features for both Cloud and Edge environments, ensuring high performance and adaptability for diverse deployment needs. You can choose to focus on either Edge or Cloud solutions. Those interested in Edge will work on advanced computer vision algorithms and sensor-based detection methods to enable efficient real-time performance directly on devices. Those focusing on Cloud will develop scalable, high-performance motion detection solutions using cloud-based technologies, optimizing collaboration between Edge and Cloud for the best results.

• 3+ years in machine learning (computer vision domain).
• Practical experience in at least one of the following problems: classification, detection, segmentation.
• Proficient in Python3, NumPy, scikit-learn, pandas, SciPy.
• Experience with deep learning frameworks: PyTorch.
• Experience in deploying machine learning models to production.
• Good understanding of machine learning and deep learning concepts.
• Good written and spoken English.

Nice to have:

• Practical experience with GANs, VAEs.
• Probabilistic programming and Bayesian framework.
• Model optimization: pruning, quantization, knowledge distillation.
• Basic understanding of web and client-server architecture.
• Experience with asyncio, aiohttp, and other async libraries for back-end.
• Basic understanding of Big Data, understanding of difference between MapReduce and in-memory processing.
• Knowledge of algorithms and data structures.
• Experience with SQL, NoSQL.
• Familiarity with Docker, Kubernetes, Kubeflow.
• Experience with C++, Bash.

• Develop innovative machine learning algorithms.
• Implement advanced methods and technologies in model development.
• Analyze large datasets to identify correlations and patterns.
• Visualize research results for clearer interpretation.
• Tune hyperparameters and optimize algorithms.
• Improve models to enhance their performance and accuracy.
• Provide consultations and collaborate with colleagues from other departments.
• Report progress and project achievements to management.
• Explore and evaluate new technologies in the field of machine learning.
• Implement cutting-edge ideas and techniques into production processes.
• Engage in continuous self-improvement and technical skill development.
• Train and mentor junior colleagues to enhance the overall competence level within the team.
• Responsible for day-to-day research tasks, such as setting up hypotheses, gathering data for experiments, training and validating ML models, validating results, and writing a paper on your results.