# AI Engineer – Discovery at AstraZeneca

AstraZeneca is hiring an AI Engineer – Discovery. It pays $124k-$187k a year and Level rates it Builds AI ●●●●; you can [apply on AstraZeneca](https://jobsbylevel.com/go/fe7d133d-8240-4069-92f5-c6994425fa97?utm_medium=ai-agent).

AI Level 4, AI centrality 96 out of 100. US - Cambridge Kendall SQ - MA.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 4 (score 96 out of 100)
- Location: US - Cambridge Kendall SQ - MA
- Salary: $124k-$187k
- Posted: November 10, 2026
- Apply: https://jobsbylevel.com/go/fe7d133d-8240-4069-92f5-c6994425fa97?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=ai-engineer-discovery-at-astrazeneca-415611

## Prepare for this job

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## Description

Are you ready to dive into the world of transformative therapies and make a significant impact? At EsoBiotec, now part of AstraZeneca, we are setting new benchmarks for biotechnological research. Our collaboration combines AstraZeneca’s global influence and scientific innovation with EsoBiotec’s unique culture of creativity and breakthroughs in cell-based therapies and immunology. Here, your scientific passion will drive real-world impact as you contribute to life-changing treatments. We are seeking a cross-disciplinary AI Engineer to support a highly wet-lab-focused discovery research team working at the forefront of CAR-T , in vivo lentiviral delivery , and oncology . This role will help accelerate research by applying AI, machine learning, and data-centric engineering to experimental workflows, biological data, and decision-making across early discovery and platform development. The position is designed for someone who can work effectively at the interface of experimental science and computational innovation . The successful candidate will partner closely with bench scientists to understand biological questions, experimental systems, and data-generation challenges, and then translate those into practical computational approaches that improve insight generation, prioritization, and research efficiency. As a Cross-Disciplinary AI Engineer , you will work alongside researchers developing next-generation approaches in cell therapy , gene delivery , and oncology discovery . Much of the team’s work is rooted in screening, vector design, cellular profiling, functional assays, and iterative platform optimization. Your role will be to identify where AI and advanced analytics can have real impact, build fit-for-purpose models and tools, and help create stronger connections between experimental output and computational decision support. This is not a role focused primarily on medicinal chemistry or small-molecule discovery. Instead, it centers on the biological and translational challenges associated with engineered cell therapies , viral delivery systems , and the interpretation of complex experimental datasets in oncology-relevant systems. Key Responsibilities Partner with experimental scientists working in CAR-T, lentiviral delivery, cell engineering, and oncology discovery to understand key scientific questions, assay workflows, and experimental decision points. Translate wet-lab problems into AI and data science opportunities , such as construct prioritization, delivery optimization, cell-state characterization, image-based phenotyping, multimodal data integration, or prediction of experimental outcomes. Build, adapt, and apply AI/ML models to support discovery activities, including predictive models, classification models, clustering approaches, representation learning, computer vision pipelines, or LLM-enabled knowledge workflows where appropriate. Work across diverse biological datasets , such as flow cytometry, single-cell and bulk omics, imaging, functional assay readouts, vector characterization data, metadata, and experimental annotations. Develop data pipelines and analytical frameworks that improve data quality, accessibility, interoperability, and reuse across experimental programs. Create practical tools and visualizations that enable bench scientists to explore data, compare constructs or conditions, and make more informed decisions about what to test next. Collaborate cross-functionally with biologists, immunologists, gene therapy scientists, bioinformaticians, data scientists, and software/informatics partners. Evaluate and communicate model performance with attention to biological validity, robustness, interpretability, and limitations in real experimental settings. Help establish best practices for reproducible, responsible, and scientifically grounded AI use in discovery research. Monitor emerging methods in AI, computational biology, and multimodal learning, and identify opportunities to apply them meaningfully

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