Senior Technical Trainer — Machine Learning

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. 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. Preferred qualifications: 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. Benefits: 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.