AI Engineer – Discovery

Sourced from astrazeneca.wd3.myworkdayjobs.com

A cross-disciplinary AI Engineer role at EsoBiotec, part of AstraZeneca, supporting a wet-lab discovery research team in CAR-T, in vivo lentiviral delivery, and oncology. The engineer translates biological questions into practical AI/ML and data-centric solutions, building models, pipelines, and tools that accelerate early discovery and platform development. The role sits at the interface of experimental science and computational innovation, partnering closely with bench scientists.

• Partner with experimental scientists in CAR-T, lentiviral delivery, cell engineering, and oncology discovery to understand scientific questions and assay workflows
• Translate wet-lab problems into AI/data science opportunities such as construct prioritization, delivery optimization, cell-state characterization, and image-based phenotyping
• Build and apply AI/ML models including predictive, classification, clustering, representation learning, computer vision, and LLM-enabled workflows
• Develop data pipelines and analytical frameworks across flow cytometry, omics, imaging, and functional assay data
• Create tools and visualizations that help bench scientists explore data and make decisions
• Evaluate and communicate model performance with attention to biological validity and interpretability
• Establish best practices for reproducible, responsible AI use in discovery research

• Bachelor's in computer science, machine learning, computational biology, bioinformatics, biomedical engineering, or related field
• At least 5 years' experience developing AI/ML solutions in biological, biomedical, or R&D environments
• Strong Python programming and experience with data science and machine learning frameworks
• Experience with complex experimental datasets and building practical analyses or tools
• Ability to communicate across computational and experimental disciplines
• Strong grounding in data quality, model evaluation, reproducibility, and analytical rigor

• Advanced degree (Master's or PhD)
• Experience in cell therapy, CAR-T, gene therapy, viral vector biology, or immuno-oncology
• Familiarity with flow cytometry, single-cell RNA-seq, multimodal omics, imaging, and high-dimensional assay data
• Experience with multimodal learning, representation learning, scientific image analysis, active learning, or Bayesian optimization
• Experience building user-facing tools, dashboards, or decision-support applications
• Familiarity with cloud, data engineering, or production-oriented research computing workflows