Evergreen - Machine Learning (umbrella)

TripleTen • Boston, MA

3h • Remote

About The Position

Nebius Academy is an international online learning platform dedicated to helping engineering teams master AI and cloud technologies. We specialize in creating hands-on, industry-relevant programs for B2B audiences, blending deep technical expertise with practical application. Our Machine Learning curriculum encompasses the entire ML lifecycle, covering foundational concepts, mathematical methods, supervised learning, time series forecasting, and numerical methods, all applied to real business problems. We are currently building a talent pool of experienced Data Scientists and ML practitioners for potential roles as Instructors, Authors, and Subject Matter Experts within our Machine Learning educational programs. We are seeking specialists in areas such as Machine Learning in Business, Basics of Machine Learning, Supervised Learning, Time Series in Machine Learning, Numerical Methods of Machine Learning, and related ML engineering topics. Ideal candidates possess deep technical knowledge and actively apply ML in real-world projects, with a proven ability to translate complex concepts into practical, teachable content. Experience with tools and workflows like Scikit-learn, PyTorch, XGBoost, time series libraries (Prophet, statsmodels), ML experiment tracking (MLflow, W&B), and feature engineering pipelines is highly valued. The capacity to teach others how to build and evaluate models in business contexts is a key differentiator. These are Talent Pool positions, meaning applications are reviewed continuously, and strong candidates are added to a roster for future opportunities. Roles are part-time, requiring approximately 10-15 hours per week, and can involve instructing live sessions, authoring course materials, or supporting curriculum development. Teaching sessions are compensated separately.

Requirements

• Strong hands-on technical expertise in machine learning, data science, or ML engineering.
• Ability to evaluate real-world ML tools, modeling approaches, and workflows — and distinguish practical solutions from hype.
• Experience structuring complex ML 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 complex technical topics clearly to mixed stakeholders.
• 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 or ML engineering, with a strong focus on supervised learning, time series, or applied ML in business contexts.
• Solid knowledge of Python and core ML stack: Scikit-learn, Pandas, NumPy, and familiarity with numerical methods and forecasting libraries.
• Hands-on experience building and deploying ML models in real-world settings — with concrete implementation cases and measurable impact.
• Proven track record in engineering advocacy, tech leadership, conference speaking, or mentoring.
• Strong desire to share knowledge and explain complex concepts in a clear, comprehensible 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.
• Ability to translate complex ML concepts into actionable, engaging learning experiences for professional audiences.
• 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.

Nice To Haves

• Russian or Spanish is a strong plus.
• 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 apply machine learning concepts and tools to real-world business problems.
• Conduct live, interactive training sessions and workshops.
• Prepare practical workshop scenarios and training materials in collaboration with our Instructional Designer.
• Develop reusable materials: model-building exercises, prompt libraries, challenge tasks, 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 ML 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 ML curriculum, ensuring our programs reflect real industry needs and the latest developments in machine learning and data science.
• Define topic priorities for ML learning programs targeting data scientists, ML engineers, and adjacent technical roles.
• Decompose ML and data science 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 market needs and ML trends into program strategy.
• Support the selection and evaluation of external authors and experts.
• Monitor emerging ML tools, frameworks, and workflows; 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