Careers

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Full-time·Shanghai / San Jose

Biologist

Responsible for defining the biology strategy and driving project progression across early-stage innovative drug discovery through to preclinical programs
Design in vitro and in vivo pharmacology/efficacy studies, interpret biological data, and deliver Go/No-Go recommendations
Collaborate with medicinal chemistry, DMPK, pharmacology, AI and other teams to advance molecule iteration; manage internal and external experimental resources, prepare project reports, and participate in project initiation and patent disclosure.

Full-time·Shanghai

Responsible for medicinal chemistry strategy and design for projects in small molecules, peptides and other modalities;
Analyze potency, selectivity and DMPK data, evaluate molecules jointly with biology and DMPK teams, and control project milestones and timelines;
Manage the internal medicinal chemistry team and external CROs, oversee synthesis progress and compound quality, resolve synthetic challenges, and mitigate project risks;
Work cross-functionally with biology, DMPK, pharmacology, AI, patent and other teams.

Full-time·Shanghai

Lead the architecture design, development and production implementation of the company's core AI drug discovery R&D platform;
Drive the build-out of the underlying cloud-native infrastructure and automation systems, including cloud service architecture, CI/CD pipelines, resource scheduling, service monitoring, high-availability assurance, and - DevOps practices, to support the platform under high concurrency with high reliability;
Own the development and iteration of the AI Agent workflow platform;
Continuously track the latest technology stack in cloud-native, AI engineering (LLM/AI ops), and Agent frameworks, adopt advanced technical solutions, and keep optimizing platform architecture and R&D efficiency.

Full-time·Shanghai / Singapore

Apply world-leading ML algorithms to drug discovery by leveraging extensive knowledge in the field, contributing to internally prioritized directions.
Identify and create new machine learning techniques and the data required for training.
Develop architectures and training algorithms for machine learning models.
Analyze and organize experimental results, reporting and presenting research findings clearly and effectively to other machine learning scientists and scientists from diverse disciplines.
Collaborate with cross-functional teams and experts from other fields to accomplish target tasks in projects.