Job Description

Company Overview Our client is an innovative deep tech and decision intelligence company that operates at the intersection of geospatial data, data science, and engineering. Their mission is to democratize insights from earth observation data and apply them across finance, agriculture, climate change, and infrastructure sectors, impacting millions of lives. With a commitment to societal impact and cutting-edge technology, they are dedicated to making earth observation insights accessible to all. Role Summary We are seeking a talented and experienced Machine Learning Researcher to join our team of dedicated professionals. As a Machine Learning Researcher, you will play a pivotal role in implementing and refining deep learning models and frameworks for various applications such as scene/semantic/panoptic segmentation, object classification, and synthetic data generation using earth observation data in the agriculture and infrastructure domains. Your work will contribute to solving critical problems in land use and crop classification, change monitoring, disaster management, and more. You will have the opportunity to apply your skills in a collaborative and innovative environment, contributing to the enhancement of decision-making processes that impact millions of lives. If you are interested in working in an environment that focuses on the impact on society, driven by cutting-edge technology, and where you will have the freedom to work on innovative ideas and be creative with no hierarchies, this is the place for you. Who We Are Looking For - We are seeking individuals who have a background in product companies, with a strong preference for those who have experience in SpaceTech, Defense, and Aerospace domains. 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, 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. Qualifications And 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 e.g. 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 Master\'s degree or Ph.D. in Engineering, Statistics, Mathematics or Computer Science, Machine Learning/Deep Learning domains. Benefits - Why Us Opportunity to work on a unique and futuristic technology setup. Flat organizational structure and accessibility. Best in-class leave policy. Additional allowances for learning, skill development, broadband, medical insurance coverage, etc. Skills: python,data mining,pytorch,machine learning models,transformers,machine learning,cnn,rnn,mxnet,lstms,lightning,scene/semantic/panoptic segmentation,deep learning frameworks,tensorflow,gans

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