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

siwaresystems; Riyadh; On-site; SAR 180,000 - 280,000; Full time

Job summary

siwaresystems in Riyadh is building a Systems-Level Integration team focused on smart, high-performance inertial sensors. As a Machine Learning Engineer in our Riyadh office, you will pioneer deep learning to enhance MEMS IMU sensor performance.

In the initial phase, you will focus on software, simulation, and data-driven modeling using datasets from our core engineering group. You will train models to denoise signals, fuse data, and correct stochastic errors, with cross-border collaboration to

Qualifications

• BSc or MSc in a related engineering field.
• Saudi Council of Engineers Engineer Grade membership is mandatory.
• Strong hands-on experience with deep learning and time-series data.
• Proficient in Python with good software engineering practices (unit tests, modular design).
• Git proficiency is mandatory.

Responsibilities

• Design, train, and validate neural networks for denoising and fusion of inertial data.
• Develop AI models for virtual sensing of MEMS IMU outputs.
• Build robust data pipelines for high-frequency inertial datasets.
• Collaborate with Egypt-based teams to ensure models fit edge devices (TinyML).
• Prototype ML approaches from research papers and optimize models for embedded targets (ARM Cortex-M).
• Regularly survey state-of-the-art ML research at the intersection of ML and inertial systems.
• Quantize and prune models for deployment on resource-constrained hardware.

Skills

Python proficiency

Time-series data

Deep learning

Git proficiency

Unit testing

Education

BSc or MSc in Computer/Electrical/Aerospace Eng

Tools

PyTorch

TensorFlow

ONNX

Job description

We are building a new Systems-Level Integration (SLI) team focused on Smart high-performance Inertial Sensors and Systems. As a Machine Learning Engineer in our expanding Riyadh office, you will pioneer the use of Deep Learning to enhance the raw performance of MEMS IMU sensors.
In the initial phase of this role, you will focus entirely on software, simulation, and data-driven modeling. You will work with datasets provided by our core engineering team to train models that correct stochastic errors, denoise signals, and improve sensor performance.

What You Will Do

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

• Virtual Sensing: Develop AI models that enhance MEMS IMU 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 required to regularly survey state-of-the-art academic research papers on the intersection of machine learning and inertial systems.

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

(Must-Haves)

• Education: 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.

• AI/ML Expertise: Strong hands-on experience with Deep Learning frameworks (PyTorch preferred) and a solid understanding of training models on time-series data.

• Coding: Python proficiency is mandatory, with strong software engineering practices (unit testing, modular code design).

• Version Control: Git proficiency is mandatory.

The "Nice-to-Haves" (Bonus Points)

• Sensor Fusion Knowledge: Familiarity with classical inertial navigation concepts, attitude representations, Extended Kalman Filters (EKF), or AHRS algorithms.

• Physics-Informed Neural Networks (PINNs): Understanding of how to constrain AI models using the laws of physics (e.g., kinematics).

• Edge AI: Experience with machine learning model deployment on Microcontrollers, STM32Cube.AI, or ONNX runtime.

Why Join Us

• Work on real-world AI + hardware systems, not just theoretic al models.

• Be part of a deep-tech company building advanced sensing technologies.

• Collaborate with highly specialized engineering teams across borders.

• Take ownership as a one of the founding ML engineers in Riyadh.

• Grow in an environment that embraces AI-driven innovation.