About the Role:
We are seeking a driven, creative, and collaborative AI scientist to join our newly established Chemistry AI Innovation Team at the Beijing R&D Center. This world-class team will focus on developing cutting-edge AI models to revolutionize small-molecule hit-finding and optimization. You will work in close partnership with global chemistry AI/data scientists and chemists in the UK, the US, and Sweden, enabling rapid data generation, curation, and model training with innovative computational approaches. This is a fantastic opportunity to shape the future of drug discovery through impactful AI-driven science, in a vibrant, newly formed team at the heart of AstraZeneca’s research network.
Key Responsibilities
you will:
- Design, develop, benchmark, and implement advanced AI/ML models (self-supervised and supervised) for small molecule drug discovery, including structure prediction co-folding models, affinity prediction models, and de novo design algorithms.
- Collaborate closely with expert medicinal and computational chemists, across AZ sites, to discover optimized hits as starting points for new drug discovery projects.
- Integrate machine learning with domain knowledge in chemistry, biophysics, structural biology, and drug discovery. Ensure generation of high-quality, validated predictions and incorporate new experimental and computational data into models.
- Collaborate cross-functionally in developing Chemistry AI solutions to address critical questions related to small molecule drug discovery, including predictive and agentic solutions.
- Effectively communicate complex technical concepts and results to multidisciplinary project teams and stakeholders.
- Keep abreast of the latest developments in AI for Science, computational chemistry, and structure prediction; proactively identify and evaluate innovative technologies and methodologies relevant to drug discovery.
- Identifying and building strategic partnership opportunities in China (academic or industry) to accelerate impact in AI-driven drug discovery
- Contribute to high-impact scientific publications and patent filings.
Required Qualifications
- Depending on the career level, a PhD or Master’s degree in Computer Science, Computational Chemistry, Structural Biology, or a related AI for Science discipline.
- Hands-on experience in developing and applying machine learning/deep learning models for small molecules or biologics, in both self-supervised and supervised ways.
- Experience and expertise in training, retraining, and fine-tuning AI models with new data and towards differentiated scientific applications.
- Demonstrated programming proficiency in Python (and relevant ML/AI frameworks such as TensorFlow, PyTorch, JAX).
- Experience in handling, curating, and analyzing large-scale scientific datasets.
- Ability to work collaboratively in a fast-paced, multidisciplinary, and cross-geographical research environment.
- Clear and effective communication skills, with fluency in English.
Preferred Qualifications
- Knowledge of state-of-the-art approaches in protein structure prediction, including co-folding with different modalities, e.g. proteins, ligands, oligonucleotides.
- Experience with multi-modal machine learning or integrating heterogeneous data types (such as molecules, protein structure data, experimental data).
- Familiarity with large-scale cloud computing and modern data engineering practices.
- Publication record in top-tier AI, computational chemistry, or cheminformatics. journals/conferences.
- Understanding of small molecule drug discovery and computational chemistry approaches.
Why Join Us?
AstraZeneca is a global, science-led biopharmaceutical company committed to transforming patients’ lives through innovative medicines. In Oncology R&D, we combine deep biological insight with state-of-the-art AI to accelerate molecular design and decision-making. Our teams operate in an open, collaborative environment across Beijing (China), Cambridge (UK) and Boston (USA), sharing best practice and pushing the boundaries of computational chemistry and machine learning.
At AstraZeneca’s Beijing R&D Center, you will be at the forefront of AI-driven innovation. You’ll have the opportunity to work with leading experts across chemistry and data science, leverage state-of-the-art technologies, and make a tangible impact on the next generation of medicines. We offer a collaborative, inclusive, and scientifically inspiring environment, with strong support for your professional growth.
Date Posted
29-9月-2026Closing Date
30-12月-2026AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
Skills Required
- PhD or Master's degree in Computer Science, Computational Chemistry, Structural Biology, or a related AI for Science discipline
- Hands-on experience developing and applying machine learning or deep learning models for small molecules or biologics
- Experience with both self-supervised and supervised learning approaches
- Experience training, retraining, and fine-tuning AI models with new data
- Programming proficiency in Python
- Experience with relevant machine learning or AI frameworks such as TensorFlow, PyTorch, or JAX
- Experience handling, curating, and analyzing large-scale scientific datasets
- Ability to collaborate in multidisciplinary and cross-geographical research environments
- Fluency in English and clear communication skills
- Knowledge of protein structure prediction and co-folding across multiple modalities
- Experience with multimodal machine learning and heterogeneous scientific data
- Familiarity with large-scale cloud computing and modern data engineering practices
- Publication record in top-tier AI, computational chemistry, or cheminformatics journals or conferences
- Understanding of small-molecule drug discovery and computational chemistry
AstraZeneca Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AstraZeneca and has not been reviewed or approved by AstraZeneca.
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Fair & Transparent Compensation — Pay is considered competitive across many roles when total rewards are factored in. Senior scientific and leadership bands are described with high ranges that reinforce competitiveness at upper levels.
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Strong & Reliable Incentives — Bonuses, equity eligibility in many salaried roles, and solid sales on‑target earnings with upside are emphasized as meaningful parts of compensation. These elements boost overall value even where base pay is not the very highest.
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Retirement Support — A 401(k) program with a strong company match and immediate vesting is repeatedly cited as a standout. Generous retirement support is viewed as enhancing the total package relative to peers.
AstraZeneca Insights
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