Yubo

Product Manager

• Define a clear product strategy for your squad and translate company objectives into measurable outcomes
• Own your squad roadmap and continuously prioritize the highest-impact opportunities for users and the business
• Lead Discovery & Experimentation: Use user insights, data, and market signals to identify opportunities, run experiments, measure impact, and inform product decisions.
• Partner closely with Engineering, Design, Data, Machine Learning, Marketing, Operations, and Trust & Safety teams
• Drive product initiatives end-to-end, from problem framing and discovery to launch and post-release analysis
• Balance user experience, business objectives, technical constraints, and long-term platform health
• Create alignment across stakeholders and lead without direct authority through clear communication and structured decision-making
• Continuously improve how your squad operates, learns, and delivers value

Senior Product Manager

Attorney

• Support the Legal team (2 Legal Counsels) in day-to-day tasks across privacy, regulatory, contract, and consumer law matters.
• Conduct legal research on EU digital regulations (e.g. DSA, AI Act, GDPR, Consumer Law).
• Assist in reviewing and drafting legal documents (e.g. contracts, privacy policies, internal memos).
• Help maintain internal compliance documentation (e.g. processing records, DPIAs, contract templates).
• Contribute to monitoring regulatory developments and summarizing key insights.
• Participate in cross-functional projects with teams such as Product, Trust & Safety, and Public Affairs.
• Assist with organizing legal workflows and knowledge management.
• Collaborate in the preparation of presentations or training sessions on legal topics.

Legal Internship

Operations

• Ensure guidance is structured for frontline use, internal, and outsourced (fast, unambiguous, audit or update-friendly)
• Deliver a monthly policy update brief, translating new or revised policies into clear operational guidance (rules, examples, and edge cases)
• Produce and maintain monthly-updated policy support materials (e.g., FAQs, glossaries, decision trees) reflecting the latest policy changes
• Conduct a quarterly audit of policy documentation and deliver a report with recommendations to improve clarity, usability, and audit readiness
• Maintain high-quality documentation across knowledge platforms (Confluence/Notion)
• Ensure consistency, version control, and traceability across all policy materials
• Structure documentation for ease of use, scalability, and auditability
• Track policy updates and assess operational impact (workflows, macros, QA standards, training)
• Produce change briefs summarising: intent, enforcement expectations, exceptions, and risk areas
• Partner with Enablement to deploy updates through training and hub documentation
• Analyse QA findings, escalations, appeals, and feedback loops to identify policy failure patterns
• Detect ambiguity, inconsistency, and mismatch between policy intent and operational reality
• Recommend targeted fixes (guidance updates, examples, workflow adjustments)
• Structure calibration sessions between Policy, Ops, QA, and Escalations on tricky categories
• Define “golden examples” and case libraries to anchor consistency
• Align on enforcement thresholds, severity, and edge case handling

Policy Ops Analyst

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.

Senior Product Manager

Legal Internship

Policy Ops Analyst

Staff ML Engineer