Level

AstraZeneca

Senior Scientist/Associate Principal Scientist, AI for small-molecule

AI in this role

pytorchtensorflowjax
fine-tuning

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.

This is the terminology Roberto used.  I think we should use whatever is understood in the local market, rather than restricted to AZ vocab.

Do we need to be more explicit that hiring level depends on experience?  I worry this is going to put people off.

Date Posted

24-9月-2026

Closing Date

AstraZeneca 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.

How we score this

Senior Scientist/Associate Principal Scientist, AI for small-molecule at AstraZeneca scores 99 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

Classification

AI Level 4. Building AI systems is the job itself: without AI, the role would not exist.

  1. AI Level 480 to 100
  2. AI Level 360 to 79
  3. AI Level 240 to 59
  4. AI Level 10 to 39

Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

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Skills and AI tools this role asks for

Fine TuningPyTorchTensorFlowJax

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. What are the limits of TensorFlow that you've run into, and how did you work around them?
  4. What's a project where you used Jax hands-on?
  5. How would you decide a model or AI system is ready to ship?

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  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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