Evergreen - Mathematics for Machine Learning

TripleTen • Boston, MA

4h • $40 - $150 • Remote

About The Position

Nebius Academy is an international online learning platform that provides hands-on, industry-relevant programs in AI and cloud technologies for B2B audiences. The Mathematics for Machine Learning curriculum is designed to bridge the gap between mathematical theory and practical Machine Learning (ML) implementation, covering essential topics such as linear algebra, numerical methods, optimization, and the mathematical foundations of modern ML systems. We are currently building a talent pool for ongoing roles as Instructors, Authors, and Subject Matter Experts within these programs, seeking specialists in Linear Algebra for ML, Numerical Methods of Machine Learning, optimization theory, matrix operations, and related mathematical foundations. The ideal candidate will not only possess theoretical knowledge but also actively apply mathematical methods in real ML projects and be able to translate abstract concepts into practical, teachable content. Hands-on experience with tools like NumPy, SciPy, PyTorch (autograd, tensor operations), and Scikit-learn internals is highly valued. The ability to explain the relevance of mathematics and demonstrate it through working ML models is a key differentiator. These are Talent Pool positions, meaning applications are continuously reviewed, and candidates are added to a roster for future relevant opportunities. Roles are part-time, approximately 10-15 hours per week, and can involve instructing live sessions, authoring learning materials, or supporting curriculum development. Teaching sessions are compensated separately.

Requirements

• 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.
• 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.
• 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.
• 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.
• Strong preparation habits and time management; able to commit 10–15 hours per week.

Nice To Haves

• Background in ML advocacy, tech leadership, or data science mentorship is a strong plus.

Responsibilities

• 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.
• Create the core educational content for our Mathematics for Machine Learning courses — from structure and learning objectives to lessons, assessments, and final projects.
• Collaborate 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.
• 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.

Benefits

• 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

What This Job Offers

• Job Type: Part-time
• Career Level: Senior
• Education Level: No Education Listed