{"id":1291437,"url":"https://alion.io/job/tiktok-big-data-engineer-tiktok-recommendation-architecture","title":"Big Data Engineer - TikTok Recommendation Architecture","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":3479,"ghost_share":0.202,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":95,"computed_at":"2026-10-25T05:49:26Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","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":68000,"max_usd":137000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":27},"experience_years_min":3,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Flink","optional":false},{"name":"Hadoop","optional":false},{"name":"HBase","optional":false},{"name":"Java","optional":false},{"name":"Machine Learning","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Redis","optional":false},{"name":"RocksDB","optional":false}],"status":"live","first_seen_at":"2022-01-27T10:10:32Z","employer_posted_date":"2026-09-26","last_verified_at":"2026-10-25T03:59:32Z","board_verified":true,"closed_at":null,"days_open":1731,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":1731},"description":"岗位职责 / Responsibilities\nTikTok will be prioritizing applicants who have a current right to work in Singapore, and do not require Our Company's sponsorship of a visa.\nAbout The Team\nOur Recommendation Architecture Team is responsible for building up and optimizing the architecture for our recommendation system to provide the most stable and best experience for our TikTok users.\nThe team is responsible for system stability and high availability, online services and offline data flow performance optimization, solving system bottlenecks, reducing cost overhead, building data and service mid-platform, realizing flexible and scalable high-performance storage and computing systems.\nResponsibilities \n- Design and implement a reasonable offline data architecture for large-scale recommendation systems\n- Design and implement flexible, scalable, stable and high-performance storage and computing systems\n- Trouble-shooting of the production system, design and implement the necessary mechanisms and tools to ensure the stability of the overall operation of the production system\n- Build industry-leading distributed systems such as storage and computing to provide reliable infrastructure for massive data and large-scale business systems\n- Develop and implement techniques and analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualisation software\n- Apply data mining, data modelling, natural language processing, and machine learning to extract and analyse information from large structured and unstructured datasets\n- Visualise, interpret, and report data findings and may create dynamic data reports as well\n任职要求 / Requirements\nMinimum Qualification(s)\n1. Bachelor's degree or above in computer science, software engineering, or a related field \n2. Familiar with many open source frameworks in the field of big data, e.g.Hadoop, Hive,Flink, FlinkSQL,Spark, Kafka, HBase, Redis, RocksDB, ElasticSearch etc.\n3. Familiar with Java, C ++ and other programming languages\n4. Strong coding and trouble shooting ability\n5. Willing to challenge questions that have no obvious answers, and have a strong enthusiasm for learning new technologies\n6. At least 3 years of relevant experience\nPreferred Qualifications:\n1. 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