Job Description

Implementing deep-learning models and/or frameworks that can be used in scene/semantic/panoptic segmentation, object classification, synthetic data generation using earth observation data in agriculture and infrastructure domain. Design and implement performance evaluations 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 and 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. Experience: 5-8 years of experience in machine learning based data product research and development in addition to 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 include 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, optimize for machine learning/ deep learning models Knowledge of 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 PhD in Engineering, Statistics, Mathematics or Computer Science, Machine Learning / Deep Learning domains

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