{"id":1291283,"url":"https://alion.io/job/tiktok-large-recommend-model-algorithm-engineer-global-e-commerce","title":"Large Recommend Model Algorithm Engineer - Global E-Commerce","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":75,"open_postings":1625,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-28T05:45:00Z"}},"role":"Industrial Engineering","role_family":"Industrial Engineering","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":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":45000,"max_usd":105000,"period":"year","method":"role_country_seniority_unknown","sample_n":782},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Post-training","optional":true},{"name":"Pre-training","optional":true}],"status":"live","first_seen_at":"2025-11-18T03:44:17Z","employer_posted_date":"2026-09-26","last_verified_at":"2026-09-26T22:09:28Z","board_verified":false,"closed_at":null,"days_open":314,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":314},"description":"岗位职责 / Responsibilities\nAbout the Team\nThe E-commerce Recommendation Foundation team is dedicated to building the next-generation recommendation intelligence. We aim to develop a unified Foundation Model that supports multi-business and multi-scenario recommendation systems, covering the full pipeline from retrieval and ranking to re-ranking, and driving a comprehensive upgrade in intelligence and generative capability.\nWe believe the future of recommendation systems goes beyond predicting click-through rates - it lies in understanding the relationship between people and content, and in generating new connections. The team is exploring an event-sequence-driven generative recommendation paradigm, deeply integrating large language models (LLMs), multimodal understanding, reinforcement learning, and system optimization to advance recommendation systems toward general-purpose intelligent agents.\nWe value original exploration and encourage both research thinking and engineering excellence. Every team member is empowered to propose hypotheses and validate ideas in an open environment - your code and papers may help define the next paradigm of recommendation systems. We seek individuals with a general intelligence mindset to join us in redefining the future of recommendation.\nResponsibilities\nBuild and optimize cross-scenario shared Foundation Models to enable unified modeling and efficient inference.\nAdvance the event-sequence-driven generative recommendation paradigm, integrating multimodal understanding and generative capabilities.\nApply LLM technologies across retrieval, ranking, and re-ranking stages; participate in model training, inference optimization, and system co-design.\nExplore the integration of LLMs / VLMs with recommendation systems to develop adaptive and evolving intelligent recommenders.\nResearch end-to-end generative recommendation and system optimization methods that balance efficiency and user experience.\n任职要求 / Requirements\nMinimum Qualifications\n- Solid theoretical foundation in machine learning, deep learning, or information retrieval.\n- Proficiency in Python and familiarity with mainstream deep learning frameworks (e.g., PyTorch).\n- Strong passion for intelligent recommendation systems and a self-driven research mindset.\nPreferred Qualifications\n- Experience in large-scale recommendation system development or large-model training, with notable technical achievements in a sub-area.\n- Research experience or publications in LLMs, multimodal learning, reinforcement learning, or generative recommendation.\n- Familiarity with pre-training and post-training processes for large language models (LLMs) or Foundation Models.","description_format":"text","description_chars":2678,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commerce","Advertising","Social Commerce","Streaming & OTT Platforms"],"lifecycle":[{"event":"open","at":"2026-09-26T07:50:22Z"}],"liveness":{"score":2,"band":"cold","label":"Long shot","p_open":1,"p_active":0.071,"p_room":0.28,"age_days":314,"expected_fill_days":21,"reasons":["conf:31","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-09-28T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/tiktok-large-recommend-model-algorithm-engineer-global-e-commerce","json_url":"https://alion.io/job/tiktok-large-recommend-model-algorithm-engineer-global-e-commerce.json","meta":{"generated_at":"2026-09-29T02:59:36Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2656,"day_limit":5000,"remaining_today":2344,"minute_limit":60,"resets_at":"2026-09-30T00:00:00Z"}}}