Evergreen - Mathematics for Machine Learning

Description

Nebius Academy is an international online learning platform helping engineering teams master AI and cloud technologies. We build hands-on, industry-relevant programs for B2B audiences — combining deep technical expertise with real-world application. Our Mathematics for Machine Learning curriculum bridges the gap between mathematical theory and practical ML implementation — covering linear algebra, numerical methods, optimization, and the mathematical foundations that power modern ML systems.

Who are we looking for? We are building a talent pool of experienced Data Scientists, ML Engineers, and Applied Mathematicians for ongoing roles as Instructors, Authors, and Subject Matter Experts in our Mathematics for Machine Learning programs.

We are looking for specialists across the following areas: Linear Algebra for ML, Numerical Methods of Machine Learning, optimization theory, matrix operations, and adjacent mathematical foundations of machine learning.

A strong candidate doesn't just know the theory — they actively apply mathematical methods in real ML projects and can translate abstract concepts into practical, teachable content. We prioritize hands-on experience with tools and workflows such as NumPy, SciPy, PyTorch (autograd, tensor operations), Scikit-learn internals, or similar. The ability to explain why the math matters — and demonstrate it through working ML models — is what sets our experts apart.

These are Talent Pool positions — we continuously review applications and build our roster of experts. This means there may not be an immediate opening at the time you apply, but strong candidates will be added to our talent pool and contacted as relevant opportunities arise.

You can join us on a part-time basis (~10–15h/week), contributing as an instructor leading live sessions and workshops, as a course author creating learning materials, or as a subject matter expert supporting curriculum development. Teaching sessions are compensated separately.

Compensation: $40–150/hour, depending on experience and format of collaboration.

Our selection process is fully asynchronous and designed to respect your time:

1. Application Review — we evaluate your profile against our current needs
2. Async Video Interview — a short self-recorded interview (10–15 minutes max)
3. Test Assignment — approximately 1 hour to complete
4. Talent Pool — finalists are added to our active roster of vetted experts
5. Hiring Manager & Tech Expert Call — once a relevant position opens, we invite you to a live interview with our team
6. Offer — we extend an offer for a relevant position upon successful completion of the process

Apply now — we review applications on an ongoing basis.

Please submit your resume in English.

What you will do

Available Roles

We are building a talent pool of Instructors, Authors, and Subject Matter Experts for our Mathematics for Machine Learning educational programs. We hire on an ongoing basis across the following specializations:

Most in demand: Linear Algebra for ML, Numerical Methods of Machine Learning Also relevant: Mathematical foundations of supervised learning, optimization theory, probability and statistics for ML, and adjacent applied mathematics topics

Instructor You will lead live, hands-on training sessions for experienced data practitioners, helping them build a deep understanding of the mathematical foundations that power modern ML systems — and apply them confidently in real projects.

• Conduct live, interactive training sessions and workshops
• Prepare practical workshop scenarios and training materials in collaboration with our Instructional Designer
• Develop reusable materials: worked examples, derivation walkthroughs, coding exercises (NumPy, SciPy, PyTorch), and reference guides
• Work with the curriculum team to ensure alignment between asynchronous and live content
• Communicate with students during Q&A sessions
• Review and incorporate learner feedback to continuously improve session design

Author You will create the core educational content for our Mathematics for Machine Learning courses — from structure and learning objectives to lessons, assessments, and final projects.

• Collaborate with us to define the course structure and learning objectives for each module
• Create clear, concise, and comprehensive content: lessons, manuals, guides, session outlines, and assessments
• Prepare content in multiple formats: text, draft slides, and screencasts
• Participate as a