Senior Technical Trainer — Machine Learning

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Technical Trainer — Machine Learning based in Saudi Arabia. We are seeking a hands-on machine learning practitioner with a passion for teaching to design and deliver engaging technical courses for engineers. In this remote freelance role, you will help learners progress from core machine learning concepts to training and evaluating their own models through practical, lab-based learning. You will collaborate with curriculum specialists and production teams to develop clear lessons, hands-on exercises, and real-world demonstrations. The focus is on sharing your technical expertise through compelling scripts, video presentations, and lab walkthroughs rather than managing production tasks. The initial paid course project typically lasts around three months and can be completed alongside existing professional commitments. With flexible hours and a delivery plan agreed in advance, the opportunity may lead to further projects or a full-time position. Accountabilities • Design and develop course content: Collaborate with the curriculum team to define course structure, sections, lessons, practical labs, and notebooks that support progressive learning. Create hands-on learning experiences: Write detailed lab specifications for the dedicated lab development team, then test the completed environments to ensure they work as intended. Write engaging educational scripts: Develop short, story-driven video lessons of approximately six minutes each, making machine learning concepts accessible, intuitive, and relevant to engineers. Deliver technical presentations: Narrate scripted lessons confidently in English and record demonstrations and lab-solution walkthroughs that reinforce key concepts. Apply practical machine learning expertise: Build course material grounded in real experience with training, evaluating, and deploying machine learning models. Incorporate editorial feedback: Work through structured reviews, particularly during the initial course section, and refine content to meet established quality and teaching standards. Collaborate remotely: Coordinate with curriculum and production teams through weekly syncs and Slack, maintaining sufficient time-zone overlap with colleagues based in Europe and Asia. Focus on teaching and technical content: Partner with the production team, which manages video editing, animation, lab infrastructure, and publishing. Competitive project-based compensation: A fixed fee is agreed upfront for each course, based on its length and technical complexity, with competitive rates for advanced topics and exceptional instructors. Flexible working arrangements: Work remotely from anywhere in the world, with no fixed weekly-hour requirement and a delivery schedule agreed in advance. Compatible with existing commitments: Complete the initial freelance course project alongside your current job or other professional responsibilities. Defined project scope: The initial course project typically takes around three months, providing a clear timeframe for planning and delivery. Payment upon completion: Receive the agreed course fee when the completed course is delivered. Long-term career potential: Successful collaboration may lead to additional course projects or a full-time opportunity as part of a growing trainer network. Dedicated production support: Focus on instructional content and teaching while the production team handles editing, animation, lab infrastructure, and publishing. Collaborative remote environment: Work closely with curriculum and production specialists through regular team syncs and Slack communication. Professional visibility: Showcase your technical expertise and teaching ability by developing practical learning experiences for a broad engineering audience. How the employer works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Data Privacy Notice: By submitting your application, you acknowledge that the employer will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. LI-CL1.

Requirements

• Hands-on machine learning experience: Demonstrated ability to train, evaluate, and ship models using frameworks such as scikit-learn, PyTorch, or TensorFlow
• Strong technical communication skills: Ability to explain machine learning concepts, including the reasoning behind different approaches, clearly to engineers who are new to the field
• Excellent English proficiency: Strong written and spoken English, with the ability to write natural scripts, deliver clear narration, and communicate confidently on camera
• A genuine passion for teaching: Evidence of enjoying knowledge-sharing through online courses, tutorials, technical talks, workshops, mentoring, or other educational activities
• Current technical knowledge: An active interest in machine learning developments and a commitment to continuously learning and improving
• Presentation confidence: Comfort presenting technical material on camera in a clear, engaging, and accessible way
• Openness to feedback: Willingness to follow an established teaching methodology, collaborate with editors, and improve course content through structured feedback
• Experience publishing courses on platforms such as Udemy, Pluralsight, or Coursera, or creating educational content on YouTube
• Experience writing a technical book or teaching at a university, bootcamp, or similar educational setting
• Practical knowledge of fine-tuning, MLOps, or deploying machine learning models in production
• Cloud machine learning certifications related to AWS, Microsoft Azure, Google Cloud Platform, or Databricks

Skills