As an AI Researcher, you will lead the research and development of novel AI models, algorithms, and systems, and deploy your research results into real-world products.
This role goes beyond theoretical work — you will collaborate closely with engineers to bring research prototypes into production, building scalable and reliable systems.
You will design the foundations of AI agent lifecycles (memory, planning, action loops), as well as high-precision information retrieval and integration pipelines, driving the core technologies of Xinobi AI.
【Responsibilities】Propose, validate, and implement novel AI models, algorithms, and systems into production
Collaborate with engineers and PMs to define research roadmaps and co-author technical reports
Architect and implement backend systems supporting autonomous agent lifecycles (episodic/semantic memory, planning, action loops)
Build memory layers using vector databases (Faiss, Milvus, Weaviate, Pinecone, pgvector) for short-, mid-, and long-term storage (including summarization, decay, retrieval pipelines)
Implement hybrid search (BM25 + dense embeddings), re-ranking with cross-encoders, metadata filtering, and semantic caching
Develop secure, high-throughput infrastructures enabling agent-to-data/model interactions (rate limiting, backpressure, fault tolerance)
Design robust APIs and asynchronous state machines enabling composable agent behaviors and tool integrations
Collaborate with ML engineers to integrate LLMs, embeddings, tool use, and reinforcement learning components, optimizing retrieval-augmented generation (RAG) workflows
Ensure performance, reliability, and observability across distributed systems, including full-stack tracing from retrieval to model outputs
【Required Qualifications (MUST)】
Professional experience in Computer Science, Machine Learning, Data Science, or related fields
Experience building backend or agent runtime systems in production, particularly with persistent memory and retrieval layers
Strong understanding of distributed systems, API design, asynchronous workflows, and search/retrieval architecture
Proficiency in Python
Familiarity with LLMs, embeddings, vector stores, and re-ranking methods
Experience tuning ANN indexes, embedding pipelines, and retrieval quality metrics (e.g., nDCG, MRR)
A product-focused mindset with the ability to iterate quickly in startup environments
【Preferred Qualifications (WANT)】
Experience training or fine-tuning large language models (LLMs)
Knowledge of reinforcement learning or probabilistic modeling
Experience running large-scale distributed experiments on GPU/TPU clusters
Familiarity with CI/CD pipelines, MLOps platforms, and monitoring/observability tools
Track record of publications, conference presentations, or open-source contributions
Experience collaborating in multicultural and cross-functional teams
【ポジション概要】
AIリサーチャーは、新しいAIモデル・アルゴリズム・システムの研究開発を担い、研究成果を実際のプロダクトへと展開していただきます。
単なる理論研究にとどまらず、エンジニアと連携して リサーチプロトタイプを本番環境へ実装 し、スケーラブルかつ信頼性のあるシステムを構築する役割です。
AIエージェントのライフサイクル(メモリ、計画、行動ループ)を支える基盤構築や、高精度な情報検索・統合パイプラインを設計し、Xinobi AIのコア技術をリードしていただきます。
【主な業務内容】
新規AIモデル・アルゴリズム・システムの提案・検証、およびプロダクト実装
エンジニアやPMと連携した研究ロードマップ策定、技術レポート執筆
自律エージェントのライフサイクルを支えるバックエンド基盤の設計・実装(エピソディック/セマンティックメモリ、計画、行動ループ)
ベクターデータベース(Faiss, Milvus, Weaviate, Pinecone, pgvector 等)を用いた短期〜長期メモリレイヤーの構築(要約、減衰、検索パイプライン含む)
ハイブリッド検索(BM25 + Dense Embeddings)、クロスエンコーダによるリランキング、メタデータフィルタリング、セマンティックキャッシングの実装
高スループットかつセキュアなエージェント・データ/モデル間通信のインフラ開発(レート制御、バックプレッシャー、フォールトトレランス対応)
柔軟で拡張可能なエージェント挙動やツール統合を可能にするAPIや非同期ステートマシンの構築
MLエンジニアと協働し、LLM・エンベディング・ツール利用・強化学習コンポーネントを統合し、RAG(Retrieval-Augmented Generation)ワークフローを最適化
分散システム全体でのパフォーマンス、信頼性、可観測性の担保(検索からモデル出力までのフルスタックトレーシングを含む)
【必須要件(MUST)】
コンピュータサイエンス、機械学習、データサイエンス、または関連分野での実務経験
本番環境でのバックエンドまたはエージェントランタイムシステムの構築経験(特にメモリ・検索レイヤーを伴うもの)
分散システム、API設計、非同期ワークフロー、検索/情報検索アーキテクチャの深い理解
Pythonでの開発経験
LLM、エンベディング、ベクトルストア、リランキング手法への知見
ANNインデックス、エンベディングパイプライン、検索精度指標(nDCG, MRRなど)の調整経験
スタートアップ環境で迅速に試行錯誤できるプロダクト志向マインドセット
【歓迎要件(WANT)】
大規模言語モデル(LLM)の学習・ファインチューニング経験
強化学習や確率的モデリングの知識
GPU/TPU環境を用いた大規模分散実験の経験
CI/CD、MLOps基盤、モニタリングツールの利用経験
論文執筆、国際会議発表、OSS貢献などの実績
多国籍・クロスファンクショナルチームでの開発・研究経験
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