Machine Learning Engineering

As a Machine Learning Engineer (Computer Vision), you will be working on highly impactful Deep Learning projects. At Azercosmos, you will get a unique opportunity to participate in the entire development cycle of large-scale Deep Learning projects from data collection to product deployment.

Responsibilities:

• Development of state-of-the-art Computer Vision models
• Conduct research and transfer insights into practical solutions
• Development and deployment of scalable data processing pipelines
• Writing clean, maintainable, and production-grade code
• Deploying machine learning models using cloud-based solutions
• Analyzing impact and effectiveness of models in production systems
• Improving the performance of existing models and frameworks
• Inspection, labeling, and cleaning of datasets 

Requirements:

• Strong programming experience in Python
• Object-Oriented Design Patterns and Principles
• Strong theoretical and practical background in the application of Deep Learning methodologies on Image Segmentation, Object Detection and Generative Modeling tasks
• Solid coding experience in Deep Learning frameworks (e.g., TensorFlow, PyTorch)
• Strong mathematical aptitude to understand and apply advanced concepts in the scientific literature

Preferred Qualifications:

• Excellent command of the English language (reading/writing)
• Computer Science and Engineering background
• Former work experience in Satellite Imagery 
• Experience with cloud-based solutions such as Amazon AWS, Google Cloud
• Prior experience with workflow management tools (e.g., Airflow)

Benefits:

• Working on high-impact large-scale computer vision projects
• Access to High-Performance GPU servers
• Being a contributor to scholarly publications
• Supportive and inclusive environment where collaboration is encouraged and learning is shared freely
• Competitive salary

Apply:

To apply for this opportunity, please send your CV, quoting the reference “Machine Learning Engineering” in the subject line. Only successful applicants will be contacted.

Deadline: 25.05.2023