{"id":1193717,"url":"https://alion.io/job/hinca-i-ie-ue-ienjinia-llm","title":"機械学習エンジニア (LLM)","company":{"id":2257349,"name":"Thinca","domain":"thinca.co.jp","url":"https://alion.io/company/thinca","size_band":"201-500","is_staffing_agency":false,"is_intermediary":false,"listed_via":null,"ats_vendor":"HERP","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"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":"DeepSpeed","optional":false},{"name":"FSDP","optional":false},{"name":"JAX","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Pre-training","optional":false},{"name":"PyTorch","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-03-24T03:21:29Z","employer_posted_date":"2026-03-24","last_verified_at":"2026-09-24T17:53:58Z","board_verified":true,"closed_at":null,"days_open":184,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":184},"description":"機械学習エンジニア (LLM)\nDescription\n仕事概要\n私たちの会社は多くのコミニュケーションデータを保有しており、それらを活用した新たなAI機能を提供していくにあたり、独自大規模言語モデルをゼロから構築することも視野に入れてユーザーごとに最適なAI機能を提供できる基盤の構築に注力していきます。最先端の研究成果を実用的なスケーラブル・システムへと昇華させ、技術的限界を押し広げることで、社会にインパクトを与えるAI基盤の構築を目指しています。\n機械学習エンジニア（LLM）として、アーキテクチャの設計、大規模なデータパイプラインの構築、および大規模分散学習の最適化において中心的な役割を担っていただきます。最先端の技術のキャッチアップをしつつ、スピード感をもってAI基盤の構築・改善を推進してもらいます。当社のAI技術の核となるエンジンの開発に初期から関わっていただけます。\n主な業務内容\n・独自のLLMのアーキテクチャ設計から、大規模クラスタを用いた事前学習の実行・管理。\n・分散学習フレームワークを用いた学習効率の最大化とCUDAレベルでの最適化。\n・数テラバイト規模のデータセットのフィルタリング、トークナイズ、クリーニングプロセスの構築。\n・学習したモデルの性能評価、およびファインチューニングの実施。\nAbout the Team\nOur company possesses a vast and unique repository of communication data. As we move toward delivering next-generation AI features powered by this data, we are focused on building a robust foundation capable of providing optimized AI experiences for every user. This includes exploring the development of proprietary large language models (LLMs) from scratch. Our mission is to translate cutting-edge research into practical, scalable systems, pushing technical boundaries to create an AI infrastructure that delivers profound social impact.\nAbout the Role\nAs a Machine Learning Engineer (LLM), you will play a pivotal role in designing architectures, building massive data pipelines, and optimizing large-scale distributed training. You will work closely with researchers to translate theoretical breakthroughs into high-performance model weights. This is an opportunity to contribute to the core engine of our AI capabilities in a fast-paced, mission-driven environment.\nIn this role, you will\nInnovate and Train: Design and execute the pre-training strategy for our proprietary LLMs using large-scale GPU clusters and our unique communication datasets.\nOptimize Performance: Implement and enhance distributed training frameworks (e.g., DeepSpeed, Megatron-LM, FSDP) and optimize kernels for maximum hardware efficiency.\nCurate Large-Scale Data: Build and manage robust pipelines for filtering, tokenizing, and cleaning terabyte-scale communication data to ensure high-quality model input.\nEvaluate and Align: Develop rigorous evaluation benchmarks and apply alignment techniques such as SFT and RLHF to improve model utility and safety for personalized user experiences.\nCollaborate and Lead: Work across cross-functional teams to integrate research advancements into our core product offerings.\nYou might thrive in this role if you have\nStrong Foundation: A Master’s or PhD in Computer Science, Machine Learning, or a related field (or equivalent practical experience).\nDeep Learning Expertise: Demonstrated experience in implementing Transformer-based models and proficiency in deep learning frameworks like PyTorch or JAX.\nDistributed Systems Knowledge: Hands-on experience with large-scale distributed training across hundreds of GPUs and an understanding of networking/infrastructure bottlenecks.\nProactive Mindset: The ability to move fast in an environment where problems are often loosely defined, owning challenges from conception to deployment.\nRequirements\n仕事概要\n私たちの会社は多くのコミニュケーションデータを保有しており、それらを活用した新たなAI機能を提供していくにあたり、独自大規模言語モデルをゼロから構築することも視野に入れてユーザーごとに最適なAI機能を提供できる基盤の構築に注力していきます。最先端の研究成果を実用的なスケーラブル・システムへと昇華させ、技術的限界を押し広げることで、社会にインパクトを与えるAI基盤の構築を目指しています。\n機械学習エンジニア（LLM）として、アーキテクチャの設計、大規模なデータパイプラインの構築、および大規模分散学習の最適化において中心的な役割を担っていただきます。最先端の技術のキャッチアップをしつつ、スピード感をもってAI基盤の構築・改善を推進してもらいます。当社のAI技術の核となるエンジンの開発に初期から関わっていただけます。\n主な業務内容\n・独自のLLMのアーキテクチャ設計から、大規模クラスタを用いた事前学習の実行・管理。\n・分散学習フレームワークを用いた学習効率の最大化とCUDAレベルでの最適化。\n・数テラバイト規模のデータセットのフィルタリング、トークナイズ、クリーニングプロセスの構築。\n・学習したモデルの性能評価、およびファインチューニングの実施。\nAbout the Team\nOur company possesses a vast and unique repository of communication data. As we move toward delivering next-generation AI features powered by this data, we are focused on building a robust foundation capable of providing optimized AI experiences for every user. This includes exploring the development of proprietary large language models (LLMs) from scratch. Our mission is to translate cutting-edge research into practical, scalable systems, pushing technical boundaries to create an AI infrastructure that delivers profound social impact.\nAbout the Role\nAs a Machine Learning Engineer (LLM), you will play a pivotal role in designing architectures, building massive data pipelines, and optimizing large-scale distributed training. You will work closely with researchers to translate theoretical breakthroughs into high-performance model weights. This is an opportunity to contribute to the core engine of our AI capabilities in a fast-paced, mission-driven environment.\nIn this role, you will\nInnovate and Train: Design and execute the pre-training strategy for our proprietary LLMs using large-scale GPU clusters and our unique communication datasets.\nOptimize Performance: Implement and enhance distributed training frameworks (e.g., DeepSpeed, Megatron-LM, FSDP) and optimize kernels for maximum hardware efficiency.\nCurate Large-Scale Data: Build and manage robust pipelines for filtering, tokenizing, and cleaning terabyte-scale communication data to ensure high-quality model input.\nEvaluate and Align: Develop rigorous evaluation benchmarks and apply alignment techniques such as SFT and RLHF to improve model utility and safety for personalized user experiences.\nCollaborate and Lead: Work across cross-functional teams to integrate research advancements into our core product offerings.\nYou might thrive in this role if you have\nStrong Foundation: A Master’s or PhD in Computer Science, Machine Learning, or a related field (or equivalent practical experience).\nDeep Learning Expertise: Demonstrated experience in implementing Transformer-based models and proficiency in deep learning frameworks like PyTorch or JAX.\nDistributed Systems Knowledge: Hands-on experience with large-scale distributed training across hundreds of GPUs and an understanding of networking/infrastructure bottlenecks.\nProactive Mindset: The ability to move fast in an environment where problems are often loosely defined, owning challenges from conception to deployment.\nPreferred Experiences\nWe are looking for\nWorking Conditions\nSalary\n1000万円以上\n※上記は目安であり、資格・経験に応じてさらに高い給与を提示することも可能です。\n基本給：720,025円～\n固定残業代（20時間）：112,975円～\nSalary: JPY 10,000,000 or more * The amount listed above is a guideline. We are prepared to offer higher compensation based on the candidate's specific qualifications, expertise, and experience.\nLocation\n東京・大阪・福岡のいずれか（転勤なし）希望に応じます。\n東京本社\n〒101-0054\n東京都千代田区神田錦町3-17 廣瀬ビル 10F\n最寄り駅\n東京メトロ「竹橋駅」より徒歩6分\n都営地下鉄・東京メトロ「神保町駅」より徒歩8分\nJR「神田駅」より徒歩15分\n大阪オフィス\n〒564-0052\n大阪府吹田市広芝町10番8号 江坂董友ビル THE HUB 江坂南313\n福岡オフィス\n〒812-0013\n福岡県福岡市博多区博多駅東一丁目1番33号\nはかた近代ビル3階\nLocation: Tokyo, Osaka, or Fukuoka\nAs noted in the Requirements, at least 3 days of in-person attendance per week is required at one of these office locations.\nJob Type\n正社員\nWork hours\nワークスタイル\n- ハイブリッド勤務（週3出社）\n- 原則9:00~18:00勤務（クライアント対応部署との連携のため）\n※出社勤務時はフレックスタイム制利用可（コアタイム：11:00~16:00）\n休日・休暇\n完全週休2日制（土・日）\n祝日\n年末年始休暇\n有給休暇（入社月に応じて入社日に最大10日付与）\n慶弔休暇\nアニバーサリー休暇（年1回）\nProbation period\n3ヶ月（待遇・給与に変更なし）\nBenefits\n社保完備\n健康保険(ITS)\n厚生年金保険\n雇用保険\n通勤交通費（上限4万円/月）\n通信手当（3,000円/月）\nクラブ活動補助\nワクワクランチ（四半期に1回・2,000円会社負担）\nリファラル制度（条件あり）\nコーヒー、ウォーターサーバー、おやつ無料\n健康診断\n•インフルエンザ予防接種（会社負担）","description_format":"text","description_chars":7193,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":true},"security_clearance":false,"languages":[{"language":"Japanese","level":"Advanced (C1)","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-24T17:53:58Z"}],"liveness":{"score":4,"band":"cold","label":"Long shot","p_open":1,"p_active":0.129,"p_room":0.28,"age_days":184,"expected_fill_days":33,"reasons":["conf:2","win:tail","crowd:"],"computed_at":"2026-09-24T20:14:38Z"},"pay":null,"html_url":"https://alion.io/job/hinca-i-ie-ue-ienjinia-llm","json_url":"https://alion.io/job/hinca-i-ie-ue-ienjinia-llm.json","meta":{"generated_at":"2026-09-24T20:14:38Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}