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AstraZeneca

AI Engineer – Discovery

Cambridge, MA, United States
Cambridge, United States

$124,000 to $187,000 USD / year

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Job details

Pay: $124,000 to $187,000 USD / year
Apply by: May 9, 2027
Experience: At least 5 years' experience developing AI/ML solutions in biological, biomedical, or R&D environments.
Posting language: English
Posted: Nov 10, 2026

Original job 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 in CAR-T and in vivo delivery research.

Required Qualifications

Bachelor’s in computer science, machine learning, computational biology, bioinformatics, biomedical engineering, systems biology, applied mathematics, or a related field.
At least 5 years' 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

Advanced degree preferred (Master’s, or PhD)
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.

The annual base pay for this position ranges from $124K to $187K. Our positions offer eligibility for various incentives - an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.

Date Posted 10-Nov-2026 Closing Date 24-Nov-2026Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.

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Skills

Research
Decision making
Prioritization
ML
Computer science
Machine learning
Biomedical engineering
Python
Data engineering
Sales

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