Job description

Remote, worldwide · Freelance course project with a path to full-time We’re hiring on behalf of a global hands-on tech-education platform used by over a million engineers to learn DevOps, cloud, Kubernetes and AI. Its courses are built around real labs, not slides, and taught by practising engineers who are great on camera. We’re looking for a machine learning practitioner who loves teaching to create and present ML courses for engineers, from foundations to training their own models. This role suits you if you already teach as part of what you do: you’ve published courses on Udemy, Pluralsight, Coursera or YouTube, written a book, taught at a university or bootcamp, or regularly run workshops and talks. How it works - It starts with one paid course project. You can do it alongside your current job. There’s no fixed weekly-hour requirement, just a delivery plan you agree on together. A typical project takes around 3 months. - The goal is long-term. The team is growing its internal trainer pool, and the first project is how both sides find out if it’s a fit. After that, it can lead to a full-time role or more course projects. - A fixed fee per course is agreed upfront, based on course length and topic complexity. Fees are competitive, and advanced topics and exceptional instructors are paid accordingly. Payment is made on delivery of the completed course. - Fully remote with flexible hours. Expect a weekly sync, Slack communication, and some time-zone overlap with a Europe/Asia-based team. What you’ll do - Shape the course with the curriculum team: sections, lessons, labs and notebooks. - Write lab specs that a dedicated labs team builds, then test them. - Write and narrate short, story-driven video lessons (about 6 minutes each) that make ML intuitive. You’re welcome to use AI to draft, but the final script should sound like you. - Record demo and lab-solution walkthroughs. - Work through structured editorial feedback, especially on your first section. Video editing, animation, lab infrastructure and publishing are all handled by the production team. You focus on the teaching. What we’re looking for - Strong, hands-on ML foundations: you’ve trained, evaluated and shipped models yourself (scikit-learn, PyTorch or TensorFlow). - The ability to explain the “why” behind ML clearly to engineers new to it. - Excellent spoken and written English. All courses are scripted, narrated and delivered in English, so you need clear, natural, easily understood English and confidence on camera. - A genuine passion for teaching, shown through courses, tutorials, talks, workshops or mentoring you’ve done because you enjoy it. - Current with the field and keen to keep learning. - Clear, engaging spoken English and confidence on camera. - Openness to an established teaching methodology and to editorial feedback. Nice to have - Published online courses, a book, or university/bootcamp teaching experience. - Experience with fine-tuning, MLOps or deploying models in production. - Cloud ML certifications (AWS, Azure, GCP, Databricks). How to apply Send your CV or LinkedIn profile, plus a link that shows how you teach: a course, a talk, a tutorial or a short video.

Requirements

Must-haves

Remote, worldwide · Freelance course project with a path to full-time
We’re looking for a machine learning practitioner who loves teaching to create and present ML courses for engineers, from foundations to training their own models.
How it works
It starts with one paid course project. You can do it alongside your current job. There’s no fixed weekly-hour requirement, just a delivery plan you agree on together. A typical project takes around 3 months.
The goal is long-term. The team is growing its internal trainer pool, and the first project is how both sides find out if it’s a fit. After that, it can lead to a full-time role or more course projects.
Fully remote with flexible hours. Expect a weekly sync, Slack communication, and some time-zone overlap with a Europe/Asia-based team.
What you’ll do
Shape the course with the curriculum team: sections, lessons, labs and notebooks.
Write lab specs that a dedicated labs team builds, then test them.
Write and narrate short, story-driven video lessons (about 6 minutes each) that make ML intuitive. You’re welcome to use AI to draft, but the final script should sound like you.
Record demo and lab-solution walkthroughs.
Work through structured editorial feedback, especially on your first section.
Video editing, animation, lab infrastructure and publishing are all handled by the production team. You focus on the teaching.
What we’re looking for
Strong, hands-on ML foundations: you’ve trained, evaluated and shipped models yourself (scikit-learn, PyTorch or TensorFlow).
The ability to explain the “why” behind ML clearly to engineers new to it.
Excellent spoken and written English. All courses are scripted, narrated and delivered in English, so you need clear, natural, easily understood English and confidence on camera.
A genuine passion for teaching, shown through courses, tutorials, talks, workshops or mentoring you’ve done because you enjoy it.
Current with the field and keen to keep learning.
Clear, engaging spoken English and confidence on camera.
Openness to an established teaching methodology and to editorial feedback.

Nice-to-haves

Published online courses, a book, or university/bootcamp teaching experience.
Experience with fine-tuning, MLOps or deploying models in production.
Cloud ML certifications (AWS, Azure, GCP, Databricks).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

kubernetesaimachine learningmlawsazuregcp

Platform: ashby
Effort: quick