naukri
Jr. / Mid Machine Learning Engineer Time-Series & Inertial AI
Si ware systems
Riyadh Region, KSA
Mid-Senior
Onsite
Discovered 4 weeks ago
Deep Learningneural networksCNNsLSTMsTCNsTransformers
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Role Overview
Si ware systems is building a Systems-Level Integration (SLI) team focused on Smart high-performance Inertial Sensors and Systems. The Machine Learning Engineer will pioneer the use of Deep Learning to enhance the raw performance of MEMS IMU sensors.
Key Skills for This Role
Deep Learningneural networksCNNsLSTMsTCNsTransformers
Full Job Posting
Job Overview
Si ware systems is building a Systems-Level Integration (SLI) team focused on Smart high-performance Inertial Sensors and Systems. The Machine Learning Engineer will pioneer the use of Deep Learning to enhance the raw performance of MEMS IMU sensors.
Responsibilities
- Design, train, and validate neural networks (CNNs, LSTMs, TCNs, Transformers, etc.) to denoise raw inertial sensors data and fuse them for better performance.
- Develop AI models that enhance MEMS IMU sensor outputs using self-supervised or supervised learning techniques.
- Build robust data processing pipelines to handle massive, high-frequency inertial systems datasets (filtering, normalization, augmentation, and windowing).
- Work closely with the Egypt-based Systems and Firmware teams to ensure your models are designed within the computational limits of edge microcontrollers (TinyML).
- Implement ML algorithms based on published academic research papers.
- Survey state-of-the-art academic research papers on the intersection of machine learning and inertial systems.
- Quantize and prune trained PyTorch/TensorFlow models for eventual deployment on resource-constrained embedded targets (e.g., ARM Cortex-M).
Requirements
- BSc or MSc in Computer Engineering, Aerospace Engineering, Electrical/Mechanical Engineering, or a strictly related engineering field.
- SCE Membership: Engineer membership in the Saudi council of engineers (Engineer Grade category) is mandatory for this role.
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