AI Engineer – Discovery

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.