Senior Technical Trainer — Machine Learning

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 United States.

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. 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.
\Design and develop course content:

Collaborate with the curriculum team to define course structure, sections, lessons, practical labs, and notebooks that support progressive learning.
Develop short, story-driven video lessons of approximately six minutes each, making machine learning concepts accessible, intuitive, and relevant to engineers.
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.
Work through structured reviews, particularly during the initial course section, and refine content to meet established quality and teaching standards.
Collaborate remotely:

Focus on teaching and technical content:

Partner with the production team, which manages video editing, animation, lab infrastructure, and publishing.
Hands-on machine learning experience:

Demonstrated ability to train, evaluate, and ship models using frameworks such as scikit-learn, PyTorch, or TensorFlow.
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.
Evidence of enjoying knowledge-sharing through online courses, tutorials, technical talks, workshops, mentoring, or other educational activities.
An active interest in machine learning developments and a commitment to continuously learning and improving.
Comfort presenting technical material on camera in a clear, engaging, and accessible way.
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.

Competitive project-based compensation:

Flexible working arrangements:

Work remotely from anywhere in the world, with no fixed weekly-hour requirement and a delivery schedule agreed in advance.
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.
Successful collaboration may lead to additional course projects or a full-time opportunity as part of a growing trainer network.
Focus on instructional content and teaching while the production team handles editing, animation, lab infrastructure, and publishing.
Collaborative remote environment:

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