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 speaker in learning videos
• Design the final project for the course
• Work iteratively with instructional designers to improve content quality
• Ensure all content meets industry standards and aligns with course objectives
• Contribute to content updates based on student feedback analysis
• Optional: participate as an instructor in live sessions — compensated separately

Subject Matter Expert You will shape the strategic direction of our Mathematics for Machine Learning curriculum, ensuring our programs reflect real industry needs and give practitioners the mathematical depth required for serious ML work.

• Define topic priorities for math-focused ML learning programs targeting data scientists, ML engineers, and adjacent technical roles
• Decompose mathematical and computational skills into competency maps, mastery frameworks, and learning roadmaps
• Review course structures and content for technical accuracy, practical relevance, and alignment with learning outcomes
• Act as an internal authority for the Curriculum team — translating industry trends and practitioner pain points into program strategy
• Support the selection and evaluation of external authors and experts
• Monitor emerging methods, tools, and frameworks (e.g., advances in numerical computing, autodiff, optimization libraries); convert insights into recommendations for new or updated programs

⌛ All roles are part-time: 10–15 hours per week.

Requirements

Subject Matter Expert

• Strong hands-on technical expertise in applied mathematics, machine learning, or ML engineering — with deep experience in linear algebra, numerical methods, or mathematical optimization
• Ability to evaluate real-world mathematical approaches and computational methods — and distinguish what actually matters for ML practitioners from academic abstraction
• Experience structuring complex mathematical knowledge into competency maps, frameworks, skill decompositions, or curriculum logic
• Ability to review technical learning content critically and provide clear, structured feedback to authors and internal stakeholders
• Seniority level that allows autonomous work after onboarding, with strong ownership and minimal supervision
• Strong communication skills and ability to explain mathematically complex topics clearly to mixed stakeholders — including those who are strong engineers but weaker in formal math
• Availability to collaborate within European time zones
• Fluent English (written and spoken); Russian or Spanish is a strong plus

Author

• 5+ years of professional experience in data science, ML engineering, or applied mathematics, with a strong focus on linear algebra, numerical methods, or the mathematical foundations of ML models
• Solid knowledge of Python and the core mathematical computing stack: NumPy, SciPy, and familiarity with PyTorch or JAX for tensor operations and autodiff
• Hands-on experience applying mathematical methods to real ML problems — with concrete implementation cases and demonstrated impact
• Proven track record in engineering advocacy, tech leadership, conference speaking, or mentoring
• Strong desire to share knowledge and explain abstract mathematical concepts in a clear, intuitive, and practically grounded way
• Ability to work independently and take ownership of a content area
• Strong attention to detail
• Availability to dedicate approximately 10 hours per week to collaboration
• Fluent English (written and spoken); Russian or Spanish is a strong plus

Instructor

• 5+ years of experience in data science, ML engineering, or applied mathematics, with a strong focus on linear algebra, numerical methods, or the mathematical foundations of ML
• Solid knowledge of Python and the core mathematical computing stack: NumPy, SciPy, and familiarity with PyTorch or JAX for tensor operations and autodiff
• Hands-on experience applying mathematical methods in real ML workflows — with concrete implementation cases and demonstrated impact
• Ability to translate abstract mathematical concepts into actionable, engaging learning experiences for professional audiences — including those who approach math from an engineering rather than theoretical background
• Confident, collaborative, and audience-oriented facilitation style
• Background in ML advocacy, tech leadership, or data science mentorship is a strong plus
• Strong preparation habits and time management; able to commit 10–15 hours per week
• Fluent English (written and spoken); Russian or Spanish is a strong plus

What we can offer you

• The opportunity to create impactful content while maintaining your primary job: Share your expertise without leaving your current role
• Competitive hourly rate of $40-$85 USD for flexible part-time collaboration with significant impact and an amazing team!
• Remote cooperation with a schedule convenient for both you and the team: We don't focus on micromanagement
• Cross-cultural experience: Become part of an international team and connect with professionals from diverse backgrounds
• Meaningful impact: Share your knowledge and help experienced engineers advance their skills through high-quality educational content
• Participation in innovative projects: Contribute to shaping the future of programming education and AI adoption
• Professional growth: Receive feedback and develop your skills as a technical content creator and thought leader