Senior Machine Learning Engineer

We’re building an AI Tutor — a personalized learning system that leverages machine learning to adapt educational content and learning paths for each student.

We’re looking for an ML engineer who enjoys fast experimentation and prototyping, but also has proven experience delivering end-to-end production solutions with measurable impact. You’ll join a cross-functional team of experienced backend and frontend engineers, AI developers, and UX/UI specialists to create a truly new kind of learning experience.

What you will do:

• Build AI-powered platform that personalize the educational journey for thousands of TripleTen students across the US and Latin America.
• Define the direction of AI and ML development in a new product, taking ownership of key architectural and technical decisions.
• Contribute to the content and evolution of the platform itself, shaping what and how students learn through data-driven personalization.
• Prototype quickly, validate ideas, and transform successful experiments into reliable production systems.

What we can offer you:

• Fully remote and full-time collaboration with professional freedom and minimal micromanagement;
• Dynamic Team: Join a diverse, global team with experience across tech, ed-tech, and various industries;
• We use digital tools like Miro, Notion, and Google Workspace for seamless collaboration;

*At this time, we are unable to offer H-1B, L-1A/B sponsorship opportunities.

**This job description is not designed to contain a comprehensive listing of activities, duties, or responsibilities that are required. Nothing in this job description restricts management's right to assign or reassign duties and responsibilities at any time.



**TripleTen is an equal employment opportunity/affirmative action employer and considers qualified applicants for employment without regard to race, color, religion, sex, national origin, age, religion, disability, marital status, sexual orientation, gender identity/expression, protected military/veteran status, or any other legally protected factor.

• Broad ML expertise. Experience training and evaluating different model types to solve real problems such as recommendation, retrieval, ranking, or next-best-action prediction. Proven depth in one or two specific areas.
• Metrics and evaluation. Strong understanding of statistics and ML metrics; ability to measure model performance and connect it to business outcomes.
• Generative AI experience. Experience building LLM-based applications that combine models with retrieval or vector databases, external APIs, and agentic or workflow-based approaches (e.g., tool calls, MCP).
• Prototyping and production delivery. Comfortable working quickly in research and experimentation, with a track record of bringing 1–2 ML solutions into stable, maintainable production systems.
• MLOps foundations. Experience managing experiments, versioning, and monitoring models using MLflow or similar tools.
• Backend and infrastructure. Experience with backend engineering and cloud deployment (AWS, GCP, etc.); understanding how to expose models as scalable services while maintaining code quality, testing, and reproducibility.

Nice to have:

• Experience with Deep Learning frameworks (PyTorch, TensorFlow, etc.)
• Familiarity with orchestration or data workflow tools (Airflow or similar)