# AI Research Scientist at AstraZeneca

AstraZeneca is hiring an AI Research Scientist. It pays $125k-$187k a year and Level rates it Builds AI ●●●●; you can [apply on AstraZeneca](https://jobsbylevel.com/go/0ddc51e7-38db-477f-b8ce-030e85ffc57b?utm_medium=ai-agent).

AI Level 4, AI centrality 97 out of 100. US - Cambridge Kendall SQ - MA.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 4 (score 97 out of 100)
- Location: US - Cambridge Kendall SQ - MA
- Salary: $125k-$187k
- Posted: November 3, 2026
- Apply: https://jobsbylevel.com/go/0ddc51e7-38db-477f-b8ce-030e85ffc57b?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=ai-research-scientist-at-astrazeneca-8e0f0b

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## Description

Senior Scientist, AI for Oncology Clinical Development Locations: Cambridge, USA (3 days in office, 2 days remote) Join AstraZeneca: Where AI Meets Life-Changing Impact What if your AI research could help transform not only how medicines are discovered, but how they reach patients? Drug discovery has benefited enormously from advances in artificial intelligence. Yet clinical development, one of the most consequential stages in bringing new treatments to patients, remains an extraordinary opportunity for innovation. Within AstraZeneca’s AI for Clinical Development team, we are reimagining that journey. Our ambition is to use AI, machine learning, statistics, and computational science to help bring safe and effective treatments to patients faster, more efficiently, and with less burden on those participating in clinical research. We are looking for a Senior Scientist, AI for Oncology Clinical Development who can combine outstanding machine learning expertise with the curiosity and drive to solve complex, high-impact scientific problems. What you’ll do As a technical contributor, you will work in cross-functional, matrixed teams to address some of AstraZeneca’s most important clinical development questions. You will have the opportunity to: Lead the technical delivery of complex, high-stakes AI initiatives. Evaluate and develop advanced AI methods across problem definition, data strategy, governance, algorithm development, validation, and adoption. Create and validate novel AI solutions for clinical study design, execution, strategy, and decision-making. Apply data, models, and insights across cancer biology and multiple drug modalities. Collaborate across key Oncology areas, including hematology, cell therapy, antibody-drug conjugates, small molecules, and biologics. Build external collaborations with academic institutions, technology partners, and industry consortia. Represent AstraZeneca at scientific conferences, standards bodies, and peer-reviewed venues. Contribute first-author research to leading machine learning and clinical AI journals. Help shape and share technical knowledge, standards, and effective practices across the team. This is an opportunity to invent new approaches and then see them applied to real clinical-development challenges, with the potential to measurably advance AstraZeneca’s late-stage Oncology pipeline. Essential experience You will have: A PhD in a quantitative discipline such as computer science, mathematics, physics, biophysics, bioinformatics, computational biology, computational neuroscience, or biostatistics. Exceptional software-development and coding skills, including experience using frontier coding-agent frameworks. A deep understanding of machine learning fundamentals. Strong expertise in one or more of the following areas: Training and tuning foundation models Bayesian inference Temporal modelling Reinforcement learning Multimodal integration and modelling Model calibration and domain adaptation Data-centric AI, including dataset creation, curation, post-training, benchmarking, and evaluation Model and data evaluation Model interpretability Model post-training and alignment Multi-agent systems and agent harnesses Desirable experience Your application will be strengthened by experience in one or more of the following: Biology or biomedical research. Applying machine learning to molecular data, including DNA, RNA, or proteins. Working with imaging data such as radiology or microscopy. Working with clinical text, electronic health records, or clinical notes. Clinical trial design, biomarker discovery, companion diagnostics, dosing, safety, endpoints, or regulatory science. Delivering models, patents, software-as-a-medical-device filings, first-author publications, open-source projects, or standards-body contributions. Maintaining strong connections with the wider machine learning research community. Working effectively across global, matrixed organizations, sites, and therapy areas. Using

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