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Roles and Responsibilities

• Implementing deep-learning models and/or frameworks that can be used in scene/semantic/panoptic segmentation, object classification, and synthetic data generation using earth observation data in the agriculture and infrastructure domain.
• Design and implement performance evaluation frameworks for incremental and step-function improvements.
• Exploring and developing new methods for improving detection/classification performance
• Building model monitoring and active learning pipelines to address the target and data shifts happening in the domain.
• Building models to solve open research problems on LULC, crop classification, change monitoring, disaster management, etc from earth observations.
• Define and implement performance metrics for the research problems and be very methodical / organized with experiment management and performance evaluations.

Requirements

Experience:

• 5-8 years of experience in machine learning-based data product research and development in addition to a relevant educational background in the field of Computer Science, Engineering, Machine Learning, etc.
• Experience with tools/frameworks from theoretical and practical aspects of computer science and ML/DL. This includes probability, statistics, learning theory, etc.
• Prior experience working with statistical modeling techniques and deep learning fundamentals, such as CNN, RNN, Transformers, GANs, etc including sequence networks eg LSTMs
• Proficiency in any programming language, preferably Python, with hands-on experience in using ML frameworks such as Tensorflow/Pytorch, Pytorch Lightning, MxNet, etc
• Ability to design, develop, and optimize for machine learning/ deep learning models
• Knowledge of the application of ML for earth observation data is good to have
• Appetite for research and implementing innovative solutions for complex problems

Educational Qualifications:

• Masters degree or Ph.D. in Engineering, Statistics, Mathematics or Computer Science, Machine Learning / Deep Learning domains