NEW 株式会社ジーニー Research Engineer, LLM/Agent【JAPAN AI採用】

ブックマーク一覧

仕事内容	About JAPAN AI JAPAN AI, Inc. was established in April 2023 as a group company of Geniee, Inc. (TSE Growth Market) with the mission of dramatically expanding human potential through AI technology. We drive cutting-edge AI R&D both domestically and internationally. Our ambition goes far beyond building AI chatbots. We are building "the brain of the enterprise"ーa next-generation core system where AI autonomously executes business operations by integrating all of a company's SaaS tools. With JAPAN AI STUDIO at the center, we are implementing a world whereーgiven a databaseーno separate application is needed; AI performs the work and returns only the results. Through the transformative power of AI, we aim to create new value and contribute to the advancement of society as a whole. Join us in leading AI innovation and shaping a future where technology empowers people to achieve more. Why We're Hiring JAPAN AI STUDIO aims to function as "the brain of the enterprise"ーintegrating every SaaS tool a company uses and enabling AI agents to autonomously execute hundreds of workflows. However, realizing this vision requires breaking through "frontier challenges" that current agent technology cannot solve: Reasoning quality limits when searching and integrating information across multiple SaaS platforms Long-term memory design that retains context across extended business processes Multimodal handling that unifies text, images, audio, and structured data Low-latency inference in environments where hundreds of companies operate simultaneously Over the past year, adoption of LLM-powered agent systems has accelerated rapidlyーfrom coding and research to customer support and security. Looking ahead to a future where AI agents handle increasingly complex tasks end-to-end or in collaboration with humans, JAPAN AI is strengthening the team that will: Build more effective agents for long-horizon tasks Design coordination mechanisms for agents to collaborate at various scales and accomplish larger objectives Solve the necessary challengesーnovel harness design, infrastructure improvements, fine-tuningーto maximize agent performance Mission "Solve the problems that make today's agents give up." Take on frontier challenges that today's AI agents cannot solve. Break through the quality limits of reasoning, retrieval/planning, long-term memory, and tool use. Pave the wayーthrough researchーfor a future where hundreds of workflows running on JAPAN AI STUDIO operate smarter, faster, and more safely. Role&Expectations As a Research Engineer, you will lead cutting-edge and applied research in AI/LLM/ML: Conceive, develop, and compare different agent harnesses (memory, context compression, inter-agent communication architectures, etc.) Design and implement rigorous quantitative benchmarks for large-scale agentic tasks Support automated evaluation of models and prompts, ensuring quality across the entire lifecycleーfrom training to production Collaborate with the product organization to solve the hardest challenges in applying agents to products Create and optimize training data mixes to improve agent task performance and usability Transfer research outcomes to the Agentic Product Engineer team, raising quality across all products Writing papers is not the goal. We prioritize applying research to production and delivering results to users in a live environment serving approximately 200 companies. Why You'll Love This Role Research that powers "the brain of the enterprise"ーThis is not about improving chatbots. You will build the technical foundation for a next-generation core system where AI autonomously executes operations by integrating all enterprise SaaSーand you will do it through research. Research→Production, directly connectedーYour methods are immediately deployed to a production environment used by～200 companies. This is not research that ends with a paperーyou will feel real-world impact. Cutting-edge AI research in practiceーWork on the industry's frontier challenges: breaking reasoning quality limits, designing long-term memory, orchestrating multi-agent coordination, and more. Research and publication, side by sideーWe encourage paper publication and tech blog writing, and actively support collaboration with academic institutions and OSS communities. Impact through technology transferーTransfer your methods to the Agentic Product Engineer team and exercise leadership in raising quality across all products. Rapid-growth environmentーIn a startup that has grown to 200+people and 9 products in just 3 years, you will have significant autonomy in technical decision-making. Job Description Agent Research&Development Conceive, develop, and compare different agent harnesses (memory, context compression, inter-agent communication architectures, etc.) Research and develop new reasoning, planning, and retrieval methods Develop technologies for multimodal and long-context handling Survey, reproduce, and improve upon the latest research papers Evaluation&Benchmarking Design and implement rigorous quantitative benchmarks for large-scale agentic tasks Design synthetic data generation and evaluation benchmarks Support automated evaluation of models and prompts (across the full lifecycle from training to production) Production Problem-Solving Optimize inference latency and cost (quantization, distillation, caching, etc.) Create and optimize training data mixes Advance agent evaluation frameworks Improve quality and tune performance in production environments Knowledge Transfer&Outreach Transfer technology and mentor the Agentic Product Engineer team Collaborate with academic institutions and OSS communities Key Results (KR/Metrics) Benchmark score improvement rate (internal and public benchmarks) Number of novel methods shipped to production (per quarter) Inference latency and cost reduction rate Number of papers and technical blog posts published Number of internal knowledge transfers completed Team Structure Approximately 120 members are part of the development organization. Research Engineers work across the following groups: JAI LabーAI research and development AI&ModelーModel training and optimization Voice&TelーSpeech AI and telephony systems Closely collaborating roles: Agentic Product EngineerーAgent feature development (primary research transfer target) Agent Harness EngineerーAgent execution infrastructure AI Quality ScientistーEvaluation pipeline collaboration Product ManagerーProduct design and prioritization
経験・資格	※求人情報の応募要件全てに該当しなくても、企業様に対して内々に打診したり相談することが可能な場合もございます。一つでも当てはまる方は前向きにご検討下さい。 You May Be a Good Fit If You Master's or Ph.D. in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Mathematics, Physics, or related fields Experience developing complex agentic systems using LLMs Significant hands-on experience in software engineering and ML Experience with LLM prompt engineering and/or building products with language models Experience with large-scale model training and inference using PyTorch or JAX Deep understanding of LLM and Transformer architectures Ability to read, reproduce, and improve upon research papers Strong implementation skills in Python (production-quality code) Language requirement (at least one of the following): Japanese: Fluentーable to discuss product development without friction English: Business level Strong Candidates May Also Have Experience with large-scale reinforcement learning on language models Experience designing and implementing multi-agent systems Publications at top-tier conferences (NeurIPS, ICML, ACL, EMNLP, or equivalent) Hands-on experience implementing alignment techniques such as RLHF or DPO Experience with multimodal models (e.g., Vision-Language models) Background in agent evaluation or AI safety research Ph.D. in CS, ML, NLP, or a related field Ability to communicate research findings in English Tech Stack Languages: Python (research / framework), TypeScript / React / Next.js (frontend) / NX ML/AI: PyTorch, JAX, Transformers, vLLM, Weights&Biases Infrastructure: GCP (containers / K8s), Docker Tools: Slack, Confluence, Linear, Google Workspace, GitHub, Notion AI Dev Support: Claude Code MAX Plan, Cursor, ChatGPT, Devin Hardware: Mac (Apple Silicon), dual monitors ※更なる詳細事項は、カウンセリング（面談）時にお伝えします。
想定年収	1200 万円 ～ 2000 万円
勤務地	東京都新宿区西新宿6-8-1 住友不動産新宿オークタワー5/6階
勤務時間	10:00～19:00 ※土日祝は休業日となります ※出向の場合は、出向先の規程に準じます ================ ●Work Style Hybrid work : 3 days in office, 2 days remote Flexible working hours : Core time is negotiable Flexibility : Future consideration for more flexible work styles is possible
休日・休暇	完全週休二日制 所定休日：土・日・祝日 休暇：年次有給休暇、夏季休暇（3日）、年末年始休暇（12月31日～1月3日）、慶弔休暇
試用期間	1か月
加入保険	社会保険完備（健康保険：関東ITソフトウェア健康保険組合）
受動喫煙対策の有無	有 敷地内禁煙（屋外に喫煙場所設置）
企業データ	会社名 株式会社ジーニー 設立 2010年4月14日 資本金 100百万円 従業員数 877名（連結） 株式 ◆東証グロース上場 事業内容 広告プラットフォーム事業/マーケティングSaaS事業/海外事業/デジタルPR事業
取材班による独自解説	広告プラットフォーム事業を中心に、企業のデジタルマーケティングを支援するSaaS事業を展開。テクノロジー企業を標榜し、生成AIを使ったサービスを手掛けるJAPAN AI株式会社を2023年に立ち上げたほか、北米の大手広告テクノロジー企業Zeltoを子会社化するなど事業拡大を図っている。 創業6年で国内トップクラス規模に拡大したアドプラットフォームを有し、DSPやDMP、マーケティングオートメーション領域についても、順調にシェアを伸ばしている。DSPは広告500社、SSPはメディア20000社ほどあり、業界No.1の地位を固くしている。 Web広告などで培ったアドテクノロジーのノウハウを活かし、DOOH（Digital Out of Home）という“屋外広告 × デジタル × データ活用”の世界に参入。これにより、ただの看板売りではなく、テック × データ × 広告のクロス領域での強みを持っている。 蓄積してきたデータを活かしたマーケティングSaaS事業も好調で、CRMの領域でシェアを伸ばしてきている。今後は海外展開を含め、さらに伸ばしていく方針。 エンジニアを内製化しているため、技術力の高さが売り。
Recruiting No.	01008655000629