{"id":1167721,"url":"https://alion.io/job/astrazeneca-senior-scientistassociate-principal-scientist-ai-for-small-molecule","title":"Senior Scientist/Associate Principal Scientist, AI for small-molecule","company":{"id":13195,"name":"AstraZeneca","domain":"astrazeneca.com","url":"https://alion.io/company/astra-zeneca","size_band":"1001-5000","is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"Workday","truth_index":{"grade":"A","score":90,"open_postings":95,"ghost_share":0,"stale_share":0.6,"repost_share":0.011,"time_to_fill_p50_days":14,"computed_at":"2026-09-24T05:45:00Z"}},"role":"Sales","role_family":"Sales","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"hiring_geo_confidence":"structured","locations":["Beijing, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Edge AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"TensorFlow","optional":false},{"name":"Multimodal AI","optional":true}],"status":"live","first_seen_at":"2026-09-24T04:29:22Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-09-24T08:54:22Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the Role:\nWe 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.\nKey Responsibilities\nyou will:\nDesign, 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.\nCollaborate closely with expert medicinal and computational chemists, across AZ sites, to discover optimized hits as starting points for new drug discovery projects. \nIntegrate 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. \nCollaborate cross-functionally in developing Chemistry AI solutions to address critical questions related to small molecule drug discovery, including predictive and agentic solutions.\nEffectively communicate complex technical concepts and results to multidisciplinary project teams and stakeholders.\nKeep 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.\nIdentifying and building strategic partnership opportunities in China (academic or industry) to accelerate impact in AI-driven drug discovery\nContribute to high-impact scientific publications and patent filings.\nRequired Qualifications\nDepending on the career level, a PhD or Master’s degree in Computer Science, Computational Chemistry, Structural Biology, or a related AI for Science discipline.\nHands-on experience in developing and applying machine learning/deep learning models for small molecules or biologics, in both self-supervised and supervised ways.\nExperience and expertise in training, retraining, and fine-tuning AI models with new data and towards differentiated scientific applications.\nDemonstrated programming proficiency in Python (and relevant ML/AI frameworks such as TensorFlow, PyTorch, JAX).\nExperience in handling, curating, and analyzing large-scale scientific datasets.\nAbility to work collaboratively in a fast-paced, multidisciplinary, and cross-geographical research environment.\nClear and effective communication skills, with fluency in English.\nPreferred Qualifications\nKnowledge of state-of-the-art approaches in protein structure prediction, including co-folding with different modalities, e.g. proteins, ligands, oligonucleotides.\nExperience with multi-modal machine learning or integrating heterogeneous data types (such as molecules, protein structure data, experimental data).\nFamiliarity with large-scale cloud computing and modern data engineering practices.\nPublication record in top-tier AI, computational chemistry, or cheminformatics. journals/conferences.\nUnderstanding of small molecule drug discovery and computational chemistry approaches.\nWhy Join Us?\nAstraZeneca 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.\nAt 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.\nThis is the terminology Roberto used. I think we should use whatever is understood in the local market, rather than restricted to AZ vocab.\nDo we need to be more explicit that hiring level depends on experience? I worry this is going to put people off.\nDate Posted\n24-9月-2026Closing Date\nAstraZeneca 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.","description_format":"text","description_chars":5408,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"Advanced (C1)","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Health Care","Pharmaceuticals","Biopharma"],"lifecycle":[{"event":"open","at":"2026-09-24T04:29:22Z"}],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":0,"expected_fill_days":14,"reasons":["conf:0","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-09-24T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/astrazeneca-senior-scientistassociate-principal-scientist-ai-for-small-molecule","json_url":"https://alion.io/job/astrazeneca-senior-scientistassociate-principal-scientist-ai-for-small-molecule.json","meta":{"generated_at":"2026-09-24T09:38:31Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}