• Key Responsibilities:
• Research and develop state-of-the-art deep learning models for time-series analysis.
• Experiment with and implement cutting-edge techniques in signal processing and machine learning.
• Conduct extensive data preprocessing, cleaning, and feature engineering to optimize model input.
• Manage the complete model lifecycle in a cloud computing environment, utilizing best practices in MLOps.
• Collaborate closely with R&D teams to design solutions that align with project objectives and customer needs.
• Publish findings, contribute to scientific literature, and participate in relevant conferences.
• Required Skills:
• Advanced degree (Masters or PhD) in Machine Learning, Computer Science, Electrical Engineering, Mathematics, or related fields.
• Strong theoretical and practical knowledge of neural networks, particularly RNN, LSTM, and attention mechanisms, tailored to time-series data.
• Proficient in Python programming and experienced with PyTorch and other deep learning frameworks.
• Demonstrable experience with cloud computing platforms like GCP or AWS, focusing on distributed systems for machine learning.
• Solid background in signal processing relevant to real-time data from sensors and industrial equipment.
• Prior experience in an R&D setting, with a proven track record of innovative problem-solving and publication.
• Recommended Skills:
• Experience with advanced MLOps practices, including automated model testing, deployment, monitoring, and maintenance.
• Familiarity with additional programming languages such as R or Java, beneficial for diverse R&D projects.
• Certification in cloud technologies and machine learning, such as AWS Certified Machine Learning - Specialty or Google Professional Machine Learning Engineer.
• Active participation in the machine learning community, with contributions to open-source projects or active engagement in technical forums.
