Cross-Disciplinary AI Engineer – Discovery

Cross-Disciplinary AI Engineer – Discovery

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.

Role Summary

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 in CAR-T and in vivo delivery research.

Required Qualifications

• Bachelor’s, Master’s, or PhD in computer science, machine learning, computational biology, bioinformatics, biomedical engineering, systems biology, applied mathematics, or a related field.

• Demonstrated experience developing AI/ML solutions in biological, biomedical, or R&D environments.

• Strong programming skills in Python and experience with relevant data science and machine learning frameworks.

• Experience working with complex experimental datasets and building practical analyses or tools that support scientific decision-making.

• Ability to work effectively with wet-lab researchers and communicate clearly across computational and experimental disciplines.

• Experience handling ambiguity and translating open-ended scientific questions into structured computational approaches.

• Strong grounding in data quality, model evaluation, reproducibility, and analytical rigor.

Preferred Qualifications

• Experience in one or more of the following areas: cell therapy, CAR-T, gene therapy, viral vector biology, lentiviral systems, immuno-oncology, or functional genomics.

• Familiarity with biological data modalities relevant to this space, including flow cytometry, single-cell RNA-seq, multimodal omics, imaging, perturbation datasets, and high-dimensional assay data.

• Experience with multimodal learning, representation learning, scientific image analysis, active learning, Bayesian optimization, LLM-enabled scientific workflows, or knowledge graph approaches.

• Understanding of the practical realities of discovery-stage wet-lab research, including assay variability, small and noisy datasets, metadata challenges, and iterative hypothesis testing.

• Experience developing user-facing tools, dashboards, or decision-support applications for scientific teams.

• Familiarity with cloud, data engineering, or production-oriented workflows for research computing environments.

What Success Looks Like

Success in this role means helping a wet-lab-heavy research team generate better insights from experimental data, identify promising directions more quickly, and use AI in ways that are scientifically credible and operationally useful. You will bring value by understanding where computational methods can meaningfully support decisions around construct design, delivery strategy, cell phenotype interpretation, assay readouts, and experimental prioritization.

The strongest candidates will be technically capable, biologically curious, and highly collaborative. They will be comfortable operating in an environment where the data may be heterogeneous, the biology may be complex, and the most valuable solutions are often those that are practical, interpretable, and closely aligned to experimental reality.

Candidate Profile

The ideal candidate is a hands-on AI engineer who enjoys working close to the science. They are excited by translational challenges in oncology, cell engineering, and in vivo gene delivery, and they know how to adapt computational methods to real-world discovery settings rather than idealized datasets. They can listen to scientists, identify tractable opportunities, build useful solutions, and communicate results in a way that supports better experiments and stronger decisions.

AstraZeneca offers an environment where courage, curiosity, and collaboration thrive. With a bold vision to eliminate cancer as a cause of death, we are dedicated to transforming patient lives through innovative science. Our team is empowered to lead at every level, making smart decisions that drive our pipeline forward. Join us in pioneering collaborative research that unites academia and industry, expediting breakthroughs in some of the hardest-to-treat cancers. 

So, what is next:

Ready to make an impact? Apply now to join our mission-driven team !

To find out more:

Company site: https://www.esobiotec.com/

Group site: https://www.astrazeneca.com/

Our social media, Follow us on LinkedIn: Esobiotec and AstraZeneca

Inclusion & Diversity: https://careers.astrazeneca.com/inclusion-diversity

Career site: https://careers.astrazeneca.com/

Date Posted

19-jun.-2026

Closing Date

02-jul.-2026

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.

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 authorisation and employment eligibility verification requirements.