Jr./Mid Machine Learning Engineer Time-Series & Inertial AI

Si ware systems

Time-Series AI Development: Design, train, and validate neural networks (CNNs, LSTMs, TCNs, or Transformers) to denoise raw inertial sensors data and fuse them for better performance.

• Virtual Sensing: Develop AI models that enhance MEMS sensor outputs using self-supervised or supervised learning techniques.

• Data Pipeline Engineering: Build robust data processing pipelines to handle massive, high-frequency inertial systems datasets (filtering, normalization, augmentation, and windowing).

• Cross-Border Collaboration: Work closely with the Egypt-based Systems and Firmware teams to ensure your models are designed within the computational limits of edge microcontrollers (TinyML).

• Rapid prototyping: You will be implementing ML algorithms based on published academic research papers.

• Research: You will be asked to prepare a detailed literature review on the state of the art usages of ML in improving inertial systems performance.

• Model Optimization: Quantize and prune trained PyTorch/TensorFlow models for eventual deployment on resource-constrained embedded targets (e.g., ARM Cortex-M).