Yubo

Machine Learning Engineer

• Deliver end-to-end ML use cases (recommendation, safety algorithms, etc.).
• Ensure production readiness, scalability, and long-term maintainability.
• Balance speed of delivery with robustness and reliability.
• Define and improve the full ML lifecycle (training, deployment, monitoring, iteration).
• Establish KPIs and monitoring standards to track model performance over time.
• Ensure continuous alignment with product and safety objectives.
• Contribute to the “ML as a Platform” strategy (tools, workflows, reusable components).
• Define scalable standards for ML development across teams.
• Enable self-service ML capabilities.
• Take ownership of legacy models and realign them with current business needs.
• Improve, retrain, and integrate them into modern pipelines.
• Define standards for LLM usage (moderation, recommendation).
• Explore and implement advanced ML approaches where relevant.
• Partner with Data Engineering, MLOps, Backend Platform, and Product teams.
• Act as a bridge between ML, platform, and business stakeholders.
• Bring technical leadership and structure to ML practices across the organization.

Staff ML Engineer