Responsibilities

Design, develop, and optimize deep learning models to process time-series and related data.
Handle raw data, perform data cleaning, feature engineering, and preprocessing to ensure data quality and suitability.
Conduct model training and optimization in a cloud computing environment to achieve efficient model training and tuning.
Integrate developed machine learning models into the supOS for real-time data processing and intelligent decision-making.
Utilize MLOps tools to manage the lifecycle of models, including model deployment, monitoring, maintenance, and updates, detect and address performance issues.
Apply signal processing techniques to process raw signal data sensors and devices generated to extract valuable information and features.
Collaborate with R&D teams to meet customer requirements.
Continuously stay updated with the latest developments in machine learning and deep learning to maintain current knowledge and skills in the field.

Requirements

Graduation from a top-tier university with a degree in Machine Learning, Computer Science, Electrical Engineering, Mathematics, etc.
Experience with deep learning on time-series data, such as RNN/LSTM/Self-Attention.
Knowledge of training neural networks in cloud environments (GCP/AWS) and distributed systems.
Experience or education in signal processing, data cleaning.
Genuine work experience in software development, with a strong background in Python.
Hands-on experience with deep learning in PyTorch and MLOps tools.