{"id":3857794,"url":"https://alion.io/job/tiktok-research-engineer-agentic-video-editing-tiktok","title":"Research Engineer, Agentic Video Editing - TikTok","company":{"id":47,"name":"TikTok","domain":"tiktok.com","url":"https://alion.io/company/tiktok","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Feishu","truth_index":{"grade":"B","score":84,"open_postings":3445,"ghost_share":0.188,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":103,"computed_at":"2026-11-15T07:52:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Jose, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"C","optional":true},{"name":"FFmpeg","optional":true}],"status":"live","first_seen_at":"2026-11-03T20:02:39Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-15T18:10:09Z","board_verified":true,"closed_at":null,"days_open":12,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":10},"description":"岗位职责 / Responsibilities\nWe are building the next generation of AI-native creation tools powered by intelligent AI agents. Our mission is to transform how people create by embedding agentic AI directly into creative workflows-from AI-powered camera effect generation to AI-driven video editing and storytelling.\nOur team operates at the intersection of AI research, systems engineering, and creative product development, translating advances in agentic AI into scalable, real-world creation tools used by millions of creators. We work closely with product and design partners to ensure that sophisticated AI systems result in simple, powerful, and intuitive creative experiences.\nResponsibilities\n- Build end-to-end agentic video editing systems, working across the agent harness, backend services, and evaluation pipelines. Own scoped features and improvements from prototyping through production.\n- Develop and improve the agent harness, including planning, tool use, context management, and error recovery for multimodal editing workflows.\n- Build reliable backend services for agent execution, session management, and media processing, and improve system latency, cost, and reliability.\n- Develop evaluation and feedback loops, including LLM judges, benchmarks, and human review workflows, to measure editing quality, diagnose failures, and catch regressions.\n- Propose new ideas and adapt advances from research and industry into practical experiments, using evaluation results and user feedback to continuously improve agent capabilities and video quality.\n- Collaborate with research, engineering, and product partners to turn successful experiments into useful editing experiences for creators.\n任职要求 / Requirements\nMinimum Qualifications\n- Bachelor’s or Master’s degree in Computer Science, AI, or a related field, or equivalent practical experience.\n- Strong programming skills in Python and solid software engineering fundamentals.\n- Foundational knowledge of machine learning and large language models.\n- Hands-on experience building an LLM application, AI Agent, or multimodal AI project through coursework, research, internships, or personal projects.\n- Ability to design experiments, analyze failures, and turn findings into working improvements.\n- Interest in video creation and building useful tools for creators.\nPreferred Qualifications\n- Experience with tool-use agents, context management, or debugging and improving multi-step agent workflows.\n- Familiarity with video understanding, audio and visual processing.\n- Experience with evaluation datasets, human annotation, or model-based evaluation.\n- Familiarity with video editing tools or media processing libraries such as FFmpeg.\n- Relevant research, open-source contributions, or projects that demonstrate the ability to build and 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