Machine Learning Engineer - On Job Training

Technical Foundations

• Strong programming skills (especially Python).
• Good understanding of ML algorithms (regression, trees, clustering, SVM, neural networks, etc.).
• Knowledge of fundamental ML theory including optimization, loss functions, evaluation metrics, and overfitting/regularization.
• Familiarity with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Understanding of preprocessing techniques and data quality management.
• Experience with Git and basic API development is an advantage.

Soft Skills

Strong analytical and problem-solving abilities.
• Passion for machine learning and eagerness to understand concepts from theory to practice.
• Clear verbal and written communication skills.
• Ability to organize code, structure tasks, and work systematically.
• Motivation to learn, improve, and deliver consistently.

Behavioral Competencies

A fresh graduate who
• Loves Machine Learning and is passionate about turning theory into real-world ML solutions.
• Understands the foundations of algorithms and enjoys learning how models work under the hood.
• Cares about data quality, preprocessing, and building clean training pipelines.
• Writes clean, organized code and values reproducibility and structure.
• Communicates results clearly and can explain model behavior with confidence.
• Thrives in a fast-paced, learning-first environment and grows quickly with mentorship.
• Is eager to deploy ML models, not just experiment with them
• Takes initiative, plans well, asks for guidance when needed, and delivers on time.
• Wants a career where they build, ship, and improve ML systems that truly matter.

Language Requirements

• English (fluent written and verbal) – Mandatory
• Arabic (fluent written and verbal) – Mandatory

The Machine Learning Engineer (Fresh Graduate) will support the design, development, and deployment of machine learning models and solutions for NEXA wearables. The role requires passion for ML, strong theoretical understanding, clean coding skills, and the ability to translate data and algorithms into practical, real-world applications.

The ideal candidate is someone who is curious, motivated, and eager to grow rapidly into a professional ML engineer.

1. Machine Learning Development
• Build, train, and validate machine learning models using appropriate algorithms and techniques.
• Explore the fundamentals behind algorithms, including their theoretical basis, assumptions, limitations, and practical usage.
• Conduct experiments, compare models, tune hyperparameters, and document outcomes.

2. Data Processing & Preprocessing
• Implement preprocessing pipelines including cleaning, normalization, transformation, feature extraction, and data augmentation.
• Understand the importance of data quality and its impact on ML performance.
• Work with raw datasets and prepare them for training, testing, and production.

3. Model Deployment & Integration
• Package trained models into deployable formats (APIs, containers, services).
• Support integration with backend, data pipelines, and production systems.
• Conduct performance checks, monitoring, and iterative improvements after deployment.

4. Coding & Software Engineering
• Write clean, modular, and well-structured code that follows best practices.
• Organize experiments, maintain reproducible pipelines, and use version control (Git).
• Participate in code reviews and contribute to internal coding standards

5. Communication & Collaboration
• Present results, experiment findings, and insights clearly to both technical and non-technical stakeholders.
• Collaborate with ML engineers, data scientists, backend teams, and product teams.
• Maintain clear documentation for models, preprocessing steps, and workflows.

6. Learning & Professional Growth
• Continuously learn ML concepts, frameworks, best practices, and tools.
• Seek guidance proactively, plan tasks carefully, and deliver work on time.
• Stay updated with ML research and emerging techniques.