{"schemaVersion":"jobsearcher.job.v1","id":"733905dab4714df6a2c8cca0","url":"https://jobsearcher.com/jobs/733905dab4714df6a2c8cca0","canonicalUrl":"https://jobsearcher.com/jobs/733905dab4714df6a2c8cca0","title":"AI Data -- Data Algorithm Expert / Architect [33252}","description":"Job DescriptionResponsibilitiesCore data infrastructure & pipeline R&D. Lead the build-out of the underlying data processing engine supporting trillion-parameter code models. Design high-performance, reusable automated processing operators, and build the full-chain production pipeline from raw code crawling, cleaning, and de-identification through to high-quality training-set delivery—solving compute and storage bottlenecks at massive data scale.High-quality code corpus construction. Drive the fine-grained cleaning and mixing strategy for complex data including multilingual code, open-source projects, technical documentation, and commit history. Use heuristic rules and model-based scoring to build a competitive “golden code corpus” that ensures the highest pretraining data quality.Frontier data synthesis & test-time scaling. Track the technical paradigms of leading AI coding tools (Cursor, Composer-2, Claude Code, etc.); research and ship LLM-based high-quality code data synthesis strategies; explore data optimization methods for test-time scaling and build a data augmentation flywheel that breaks past the scale ceiling of natural code data.Evaluation-driven data iteration (EDD). Partner with the algorithm team to build scientific benchmarks for code capability, establishing a virtuous loop of data production → model evaluation → bad-case analysis → data iteration. Precisely surface model weaknesses to drive dynamic dataset adjustment and expansion.Tokenization & data alignment optimization. Given the unique nature of code (AST structure, special symbols), lead or optimize the design of a dedicated tokenization vocabulary; study the effect of different code data types on model scaling laws to improve token and training efficiency.QualificationsEducation. Bachelor's degree or above in Computer Science, AI, Mathematics, or a related field, with a strong algorithm foundation and very strong hands-on engineering ability.Big-data engineering. Proficient in Python; skilled with large-scale distributed data processing frameworks such as Spark / Flink / Ray; comfortable in Linux and able to independently develop and tune batch-and-stream processing for TB/PB-scale unlabeled text/code data.LLM & code understanding. Deep understanding of the Transformer architecture and the training flow of mainstream open-source LLMs (Qwen, Llama, DeepSeek, etc.); some grasp of code syntax and compiler principles; deep understanding of how different training stages (pretrain / SFT / RLHF) demand different data distributions and quality.Hands-on data experience. Complete project experience processing massive code/text data; mastery of core techniques including MinHash/SimHash deduplication, privacy-compliance filtering, and hard-example mining; able to abstract tedious cleaning logic into systematic standards.Research + engineering. Publications at top venues (NeurIPS, ICML, ICLR, ACL) on data-centric AI, data synthesis, RL, or efficient training are a plus; alongside strong ability to turn theory into high-performance production code.Nice-to-havesHeavy hands-on use of mainstream AI coding tools (Cursor, Composer-2, Claude Code, Copilot) with deep insight into coding-agent workflows.Familiarity with large-scale data processing frameworks (e.g., datatrove) and real experience handling trillion-scale datasets.Core-contributor experience on the data side of a well-known open-source LLM project, or strong placements in competitions such as Kaggle.Background in low-resource-language code processing, multimodal interleaved data processing, or large-scale knowledge-graph construction.","company":"Stealth Startup","rawCompany":"stealth startup","city":"Edison","state":"NJ","isRemote":false,"isActive":false,"createdAt":"2026-06-21T00:02:36.891Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"AI Data -- Data Algorithm Expert / Architect [33252}","description":"Job DescriptionResponsibilitiesCore data infrastructure & pipeline R&D. Lead the build-out of the underlying data processing engine supporting trillion-parameter code models. Design high-performance, reusable automated processing operators, and build the full-chain production pipeline from raw code crawling, cleaning, and de-identification through to high-quality training-set delivery—solving compute and storage bottlenecks at massive data scale.High-quality code corpus construction. Drive the fine-grained cleaning and mixing strategy for complex data including multilingual code, open-source projects, technical documentation, and commit history. Use heuristic rules and model-based scoring to build a competitive “golden code corpus” that ensures the highest pretraining data quality.Frontier data synthesis & test-time scaling. Track the technical paradigms of leading AI coding tools (Cursor, Composer-2, Claude Code, etc.); research and ship LLM-based high-quality code data synthesis strategies; explore data optimization methods for test-time scaling and build a data augmentation flywheel that breaks past the scale ceiling of natural code data.Evaluation-driven data iteration (EDD). Partner with the algorithm team to build scientific benchmarks for code capability, establishing a virtuous loop of data production → model evaluation → bad-case analysis → data iteration. Precisely surface model weaknesses to drive dynamic dataset adjustment and expansion.Tokenization & data alignment optimization. Given the unique nature of code (AST structure, special symbols), lead or optimize the design of a dedicated tokenization vocabulary; study the effect of different code data types on model scaling laws to improve token and training efficiency.QualificationsEducation. Bachelor's degree or above in Computer Science, AI, Mathematics, or a related field, with a strong algorithm foundation and very strong hands-on engineering ability.Big-data engineering. Proficient in Python; skilled with large-scale distributed data processing frameworks such as Spark / Flink / Ray; comfortable in Linux and able to independently develop and tune batch-and-stream processing for TB/PB-scale unlabeled text/code data.LLM & code understanding. Deep understanding of the Transformer architecture and the training flow of mainstream open-source LLMs (Qwen, Llama, DeepSeek, etc.); some grasp of code syntax and compiler principles; deep understanding of how different training stages (pretrain / SFT / RLHF) demand different data distributions and quality.Hands-on data experience. Complete project experience processing massive code/text data; mastery of core techniques including MinHash/SimHash deduplication, privacy-compliance filtering, and hard-example mining; able to abstract tedious cleaning logic into systematic standards.Research + engineering. Publications at top venues (NeurIPS, ICML, ICLR, ACL) on data-centric AI, data synthesis, RL, or efficient training are a plus; alongside strong ability to turn theory into high-performance production code.Nice-to-havesHeavy hands-on use of mainstream AI coding tools (Cursor, Composer-2, Claude Code, Copilot) with deep insight into coding-agent workflows.Familiarity with large-scale data processing frameworks (e.g., datatrove) and real experience handling trillion-scale datasets.Core-contributor experience on the data side of a well-known open-source LLM project, or strong placements in competitions such as Kaggle.Background in low-resource-language code processing, multimodal interleaved data processing, or large-scale knowledge-graph construction.","datePosted":"2026-06-21T00:02:36.891Z","dateModified":"2026-06-21T00:02:36.891Z","hiringOrganization":{"@type":"Organization","name":"Stealth Startup","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Edison","addressRegion":"NJ","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"733905dab4714df6a2c8cca0"},"url":"https://jobsearcher.com/jobs/733905dab4714df6a2c8cca0"}}