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Salary
≈ $28k – $78k per year (Estimated)
Location
In office (Beijing)
Seniority
Senior · 5+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 16, 2026. First seen by Alion on Oct 10, 2026. DP Technology scores B on the Alion truth index.

Overview
Company
Impact
Profile match
DP Technology is a Beijing-based AI for Science company that builds scientific computing and AI research platforms, including the Bohrium research platform, Hermite drug design software and Piloteye tools, plus automated laboratory and research services. Founded in 2018, it has offices and R&D centres in Beijing, Shanghai, Shenzhen and Yibin, and its US subsidiary Atombeat in Boston offers AI-driven drug discovery services to biotech and pharmaceutical companies. Its openings, mostly in Beijing, include AI agent and platform developers, algorithm and data interns, laboratory scientists for chemistry and bioassays, hardware and embedded engineers and product managers.

岗位职责 / Responsibilities

1.制定并落地全实验室的统一样品标识体系:全链路唯一 ID 编码规则、96/384 孔板的板位映射、批次与数据血缘字段、条码打印与扫码终端规范。这套规则是所有工站、所有设备、所有数据的公共坐标,也是其余全部集成工作的前置依赖;

2.定义各工站的实验结果数据结构:合成产率、纯度、保留时间、活性(IC50)、异常码的统一字段与量纲。失败孔与未命中样品的编码方式同样由本岗位定义--失败数据是模型适用域的边界信息,与成功数据同等入库,不作丢弃处理;

3.建设数据中台的入库与回流通路:结构化入库、QC 标记与不确定度、异常谱图同等入库、版本血缘。数据按"原始数据-结构化字段-专家标注-模型训练数据-对外交付数据"分层归档,样品 ID、批次、纯度、方法、设备与软件版本全程可追溯;

4.建设中台的读出面,而不只是写入面。 中台必须能被消费,否则只是数据坟场。至少覆盖四类查询:模型训练所需的数据集导出(按靶点、化学系列、数据质量分带); 药物化学家做推进/终止判断所需的结构-活性关系表与关联数据; 对外服务客户的交付数据包生成; 单个化合物的完整履历回溯(在哪块板哪个孔合成、由哪台仪器分析、用哪个批次的酶检测);

5.建设跨工站的作业排程:任务优先级、设备与物料约束下的排产、冲突裁决、异常后的重排。对外服务项目优先占用产能,未被占用的设备时段自动排入标准化数据生产任务;

6.与设备集成工程师配合,在设备模拟环境上完成调度与入库链路的端到端空跑与回归,使软件进度不被硬件到货节奏卡住;

7.与公司总部模型团队对接数据接口:实测数据入库后触发对应预测模型再训练并产出新版本号,模型版本与训练数据快照留痕、支持回滚。

任职要求 / Requirements

1.本科及以上学历,计算机、软件工程、数据科学、自动化相关专业,5年以上数据平台或后端系统开发经验,有过完整的数据链路从零搭建经历(采集→入库→治理→消费);

2.精通 Python 与 SQL,熟悉至少一种数据存储组合(关系型 + 对象存储 + 时序或文档型),能根据数据特征做合理选型;

3.具备数据结构设计与治理的实际经验:字段规范、量纲统一、版本演进与向后兼容、血缘追踪。这是本岗位最核心的能力,重于框架熟练度;

4.熟悉至少一种工作流 / 调度框架(Airflow、Temporal、Prefect、Dagster 或自研),理解 DAG、重试、幂等、补数、优先级队列;

5.具备资源约束下的排程逻辑设计能力:多任务竞争有限设备与物料时的排队、抢占与冲突裁决;

6.理解数据质量工程:校验规则、异常检测、脏数据拦截,能在入库前设门而不是入库后补救;

7.能与化学家、生物学家直接沟通,把"这个孔为什么是空的"这类实验语言翻译成字段定义。

加分项:

1.有 LIMS / ELN / SDMS 系统的开发或深度实施经验(LabWare、Thermo SampleManager、STARLIMS、Benchling,或国内厂商产品);

2.有科研数据管理规范的实践:FAIR 原则、ALCOA+ 数据完整性、审计追踪;

3.有制造业 MES / 车间排程系统经验,理解物料台账、批次追溯、工单流转--与实验室场景高度同构;

4.有机器学习数据管道经验:特征存储、训练数据版本管理、数据集快照与模型版本关联;

5.有条码 / RFID 全链路追踪系统的落地经验;

6.熟悉化学与生物数据对象(分子结构、孔板、剂量响应曲线)者优先;

7.英语可读写。

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