Lead AI & Data Platform Engineer - Marketplace (Remote)

Lifecycle Marketing Automation & In-App Personalization

• Build behavior-based lifecycle automation across our platform using buyer, seller, product, category, livestream, bidding, and purchase data.
• Design trigger-based communication flows across push notifications, email, SMS, WhatsApp, and in-app messages.
• Create personalized recommendation triggers based on user behavior, including watched livestreams, followed sellers, saved shows, category interest, viewed products, bids placed, abandoned checkout, past purchases, and similar buyer behavior.
• Build timing intelligence to decide the best moment to send each message, such as before a relevant livestream starts, after a buyer shows intent, when a seller goes live, when a similar product is listed, or when a buyer is likely to return.
• Build recommendation logic for products, livestreams, sellers, categories, auctions, and offers.
• Create automated journeys for buyer activation, first purchase, second purchase, reactivation, VIP buyers, inactive buyers, category-based buyers, and high-intent livestream viewers.
• Create automated journeys for seller activation, first livestream, first sale, seller retention, seller quality improvement, and high-potential seller support.
• Build frequency capping, quiet hours, channel prioritization, message ranking, and suppression logic to avoid spamming users.
• Connect lifecycle campaigns back to the central data warehouse to measure open rates, click-through rates, conversion, GMV, repeat purchase, retention, unsubscribe behavior, and channel performance.
• Work with marketing and product teams to test which messages, channels, timings, and recommendations drive the highest conversion and retention.
• Build the data layer needed for AI-generated personalized content, such as dynamic product recommendations, livestream reminders, category alerts, seller updates, and personalized offers.

AI Engineering & LLM-Based Automation

• Build production AI workflows that support seller onboarding, seller scoring, customer support routing, product listing improvement, content moderation assistance, campaign generation, and operational automation.
• Design and deploy LLM-based internal tools for support, seller success, marketing, product, and operations teams.
• Evaluate and integrate AI APIs, open-source models, vector databases, RAG workflows, agent frameworks, and model orchestration tools.
• Build AI systems with proper logging, evaluation, guardrails, fallback logic, human review workflows, and cost monitoring.
• Create reusable AI services and APIs that can be used across our platform.
• Keep AI features practical, measurable, and connected to business outcomes.

Personalization, Ranking & Recommendation Systems

• Build recommendation and ranking logic for live shows, sellers, products, categories, search results, and notifications.
• Create personalization models based on buyer interests, behavior, purchase history, livestream watch time, bidding activity, followed sellers, category affinity, and similar users.
• Support For You style discovery experiences for live commerce.
• Build buyer and seller intelligence models that help us identify high-potential buyers, valuable sellers, churn risks, inactive users, and growth opportunities.
• Create scoring systems for buyer levels, seller levels, lifecycle stages, and trust-based segmentation.
• Work with product and growth teams to test and improve recommendation quality.

Live Commerce AI & Media Automation

• Explore and build AI features for livestream workflows, including transcription, translation, summarization, content tagging, clip extraction, and moderation assistance.
• Work with real-time media systems such as LiveKit, WebRTC, audio/video pipelines, speech-to-text, and translation tools.
• Build automation that helps convert livestream content into reusable marketing assets, including short clips, product highlights, seller summaries, and campaign-ready content.
• Analyze livestream performance data to help sellers improve conversion, engagement, auction success, and viewer retention.

Programmatic SEO & Marketplace Content Intelligence

• Support scalable SEO systems for marketplace listings, seller pages, product pages, livestream pages, category pages, and search landing pages.
• Use AI to improve multilingual product content, metadata, structured data, search relevance, and content quality.
• Build systems that identify high-opportunity categories, keywords, listings, and content gaps.
• Ensure AI-generated content is high-quality, brand-safe, multilingual, and aligned with platform standards.

MLOps, LLMOps & Production Reliability

• Build the technical foundation for deploying, monitoring, evaluating, and improving AI systems in production.
• Implement prompt versioning, model evaluation, experiment tracking, cost monitoring, latency tracking, and output quality checks.
• Build observability around AI workflows, including errors, hallucination risk, user feedback, failed tasks, and fallback paths.
• Define when to use closed-source APIs, open-source models, fine-tuning, RAG, rule-based systems, or traditional ML.
• Ensure AI and data systems are scalable, secure, maintainable, and cost-efficient.

Data Governance, Privacy & Security

• Implement role-based access control, data permissioning, sensitive data handling, and secure data workflows.
• Ensure marketing audiences, AI workflows, and user data pipelines follow consent, privacy, and governance best practices.
• Help define data retention, anonymization, audit logs, and access policies.
• Work with leadership to ensure data is useful without becoming risky, messy, or non-compliant.

Technical Leadership & Cross-Functional Ownership

• Own the AI and data platform roadmap in partnership with product, engineering, marketing, seller success, and leadership.
• Translate business goals into technical systems and measurable outcomes.