Build the future of AI-driven drug discovery 共建 AI 驱动新药发现 的未来

Core of the DTBL Design loop. You will develop and deploy generative models for structure-aware 3D molecular design, directly powering all four pipeline programs. DTBL Design 环节核心。你将开发并部署结构感知三维分子生成模型，直接驱动四条管线。

Core of the DTBL Learn loop. You will build and maintain multi-task SAR models and ADMET prediction pipelines that route every generated molecule through a druggability filter before synthesis. DTBL Learn 环节核心。你将构建并维护多任务 SAR 模型与 ADMET 预测管线， 对每个生成分子在合成前完成成药性过滤。

The hidden core of the DTBL loop. You will own dry-wet experiment data standardization, feature engineering, and version management — the data quality layer that determines whether our AI models improve or stagnate. Industry data shows 68% of AI projects fail due to poor data quality; this role exists to ensure GenTide is not one of them. DTBL 闭环的隐性核心。你将负责干湿实验数据标准化入库、特征工程与版本管理—— 这一数据质量层决定我们的 AI 模型是持续进步还是停滞不前。 行业数据显示 68% 的 AI 项目因数据质量差而失败；这个岗位就是为确保睿联不是其中之一。

One of the most critical scientific roles at GenTide. You will lead lead compound design strategy across all programs, interpret SAR data, and serve as the primary interface between AI-generated candidates and real-world synthesis. GenTide 最关键的科学岗位之一。你将主导各管线先导化合物的设计策略、 解读 SAR 数据，并作为 AI 生成候选物与真实合成之间的核心桥梁。

Synthesize AI-designed candidate molecules from mg to g scale. Work closely with medicinal chemistry and the AI team to rapidly iterate on compound series across GLP-1, Nav1.8, and TKI programs. 合成 AI 设计的候选分子（mg 至 g 级）。与药物化学和 AI 团队紧密协作， 快速迭代 GLP-1、Nav1.8 与 TKI 管线化合物系列。

One of GenTide's core differentiators. You will own our Cryo-EM pipeline — sample preparation, data collection, and 3D reconstruction — delivering <2.5 Å binding structures that feed directly into AI model retraining. GenTide 核心差异化能力之一。你将主导冷冻电镜全流程——样品制备、数据采集与三维重构—— 输出 <2.5 Å 结合结构，直接反哺 AI 模型迭代。

Design and execute in vitro assays for all four pipeline programs. Operate HTS dual-cell systems, receptor binding, and functional assays that generate the activity data feeding the DTBL closed loop. 为四条管线设计并执行体外评价实验。操作 HTS 双细胞系、受体结合实验与功能测试， 产出驱动 DTBL 闭环的活性数据。

A critical-path role. IND CMC documentation requires 12–18 months of preparation. You will join from the Lead Optimization stage to review formulation feasibility of AI-designed molecules early — preventing the common "AI design vs. formulation" disconnect that stalls many programs. 关键路径岗位。IND CMC 文件需要 12–18 个月准备周期。你将从先导优化阶段加入， 提前评估 AI 设计分子的配方可行性——防止许多项目在此环节出现的"AI 设计与配方脱节"问题。

Own reagent procurement, instrument maintenance, vendor management, and lab compliance. A well-run lab is a force multiplier for every scientist on the team. 负责试剂采购、仪器维护、供应商管理与实验室合规。 高效运转的实验室是每位科学家效能的倍增器。