Senior Technical Trainer — Machine Learning Remote, worldwide

OnHires; Oregon; Hybrid; USD 3,000 - 9,000; Part time

Benefits offered by this job

Fully remote

Flexible hours

Project-based freelance work

Job summary

OnHires is seeking a remote freelance ML educator to design and present courses for engineers. You will shape the curriculum, craft labs and notebooks, and narrate short video lessons that explain ML concepts clearly and accessibly.

This role emphasizes practical labs, real-world models, and engaging on-camera delivery. Ideal candidates have published courses or taught in university/bootcamp settings, with strong English skills and a passion for teaching.

Qualifications

• Strong, hands-on ML foundations and ability to ship models.
• Ability to explain the "why" behind ML clearly to engineers new to it.
• Excellent spoken and written English; scripting and on-camera delivery.

Responsibilities

• Shape the course with curriculum team: sections, lessons, labs, notebooks.
• Write lab specs and test them with dedicated labs team.
• Write and narrate short, story-driven video lessons (approx. 6 minutes each).
• Record demo and lab-solution walkthroughs.
• Incorporate editorial feedback, especially on first section.

Skills

ML foundations

Explain ML clearly

Story-driven narration

English communication

Tools

PyTorch

scikit-learn

TensorFlow

Job description

Remote, worldwide Freelance course project with a path to full-timeWe'7 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 ).