{"id":1236728,"url":"https://alion.io/job/tiktok-machine-learning-engineer-content-ecology-creator-e-commerce-governance","title":"Machine Learning Engineer (Content Ecology & Creator) -E-commerce Governance","company":{"id":47,"name":"TikTok","domain":"tiktok.com","url":"https://alion.io/company/tiktok","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Feishu","truth_index":{"grade":"B","score":83,"open_postings":3427,"ghost_share":0.193,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":101,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Seattle, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":162000,"max_usd":358000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":348},"experience_years_min":1,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"GNN","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"RAG","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"DPO","optional":true},{"name":"RLHF","optional":true}],"status":"live","first_seen_at":"2026-01-15T02:54:54Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-18T21:13:29Z","board_verified":true,"closed_at":null,"days_open":276,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":276},"description":"岗位职责 / Responsibilities\nGlobal E-Commerce | Governance & Experience Algorithm Team\nAbout the TeamBuilding a Prosperous, Trusted, and Fair Global E-Commerce Ecosystem\nWe are the Governance & Experience Algorithm Team, the AI guardians ensuring the long-term health of TikTok Shop’s global platform.\nAs our international business expands, our mission goes beyond traditional risk control. We are dedicated to constructing a prosperous, trusted content ecosystem and maintaining a fair, healthy environment for creators.\nWe leverage LLM agents, RAG, GNN, and Sequence Modeling to solve complex governance challenges. We don't just block bad actors; we shape the rules of the game to ensure that creativity is rewarded, fairness is upheld, and the ecosystem thrives.\nOur Core Mission:\n- Trust & Quality: Ensuring users trust what they see, establishing a standard where \"Good Content = Good Business.\"\n- Creator Governance: Managing the full lifecycle of creators by identifying malicious intent (e.g., piracy, content mills) while protecting high-potential authentic creators.\n- Ecosystem Fairness: using AI to ensure fair traffic distribution and prevent monopolies by bad actors, fostering a diverse and sustainable creator community.\nWhat You’ll Do\n1. Creator Governance & Quality Modeling\n- Signal-Driven Creator Profiling: aggregated underlying multi-modal signals (e.g., static frames, low-aesthetic detection, piracy fingerprints) to build comprehensive Creator Quality Scores.\n- Combat Low-Quality & Malicious Intent: Develop sequence-based models to detect and penalize creators engaging in \"low-effort selling,\" \"re-recording/piracy,\" and \"matrix account spamming,\" effectively purging the ecosystem of noise.\n- LLM & RAG Intelligent Governance: Build LLM + RAG systems that dynamic interpret complex governance policies. Develop agents that not only flag risky creators but provide explainable reasoning to guide creator education and improvement.\n2. Graph Intelligence & Syndicate Detection\n- Heterogeneous Graph Mining: Construct large-scale Heterogeneous Graphs (Creator-Product-Video-User) to uncover hidden relationships and organized bad actors (e.g., fake engagement rings, black-market account trading, sybil attacks).\n- Cross-Domain Risk Propagation: Utilize graph algorithms to track how risk propagates across different scenarios (Content vs. Shelf) and markets, predicting where bad actors will migrate next.\n3. Ecosystem Strategy, Fairness & Optimization\n- Multi-Objective Optimization (MMoE/PLE): Develop advanced multi-task learning models to balance conflicting objectives-maximizing Ecosystem Prosperity and GMV while minimizing Governance Risk and User Complaints.\n- Fairness Algorithms: Design traffic regulation strategies that prevent the \"rich get richer\" effect for low-quality diverse content, ensuring fair exposure for high-quality, original creators.\n任职要求 / Requirements\nMinimum Qualifications:\n- Bachelor's degree or above in computer science or related field\n- Proficient in Python/C++ with strong hands-on experience in PyTorch or TensorFlow\n- Deep expertise in at least one of the following areas: NLP/LLM (Agents/Tuning), Graph Neural Networks (GNN), Sequence Modeling, or Machine Learning\n- 1+ years of experience in Content Governance, Trust & Safety, Creator Ecology, or Advertising/Search/Recommendation\nPreferred Qualifications\n- Cutting-Edge Application: Experience with RAG, DPO/RLHF, or Multi-Modal Representation Learning in a production environment is highly preferred\n- You view problems through an ecosystem lens-caring about Health, Fairness, and Diversity, not just binary classification metrics (Precision/Recall)\n- Ability to translate abstract business goals (e.g., \"Improve Creator Fairness\") into concrete mathematical definitions and model targets\n- Strong communication skills to articulate algorithmic strategies to Policy, Operations, and Product teams\n- You enjoy the \"cat and mouse\" game of outsmarting evolving bad actor 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