Generative AI Engineer

Job Description:

We are seeking a Generative AI Engineer to specialize in biological data, including RNA-seq, Digital Pathology (Multiplexed Immunofluorescence, Hematoxylin, and Eosin staining) and other types of biological data. In this role, you will develop and implement single-modality encoders (e.g., VAE, LIAE, DINOv2) and multimodal deep learning models (e.g., Stable Diffusion based and/or DDPM) using this data. The position also involves interpreting complex biological data and ensuring adherence to Good Clinical Practice (GCP) and Good Laboratory Practice (GCLP) standards.

Job responsibilities

• Analyze and interpret single-modality data, including, but not limited to RNAseq, histology, cfRNA, and xCR.
• Develop and implement Python-based computer vision algorithms for image analysis, encompassing data preprocessing, image segmentation, feature extraction, and classification.
• Maintain detailed records of all analyses to ensure data integrity and reproducibility.
• Stay updated with the latest advancements in computer vision, imaging, and deep learning technologies.
• Ensure all activities comply with GCP and GCLP standards.
• Integrate with LLM API for process automation and support.
• Support and develop multimodal diffusion-based models.

Required qualifications

• Bachelor’s, preferably Master’s or PhD degree in Computer Science, Bioinformatics, Biomedical Engineering, or a related field.
• At least 2 years of experience in computer vision or a related field.
• Demonstrated experience with Python-based image analysis and computer vision algorithms.
• Strong analytical and problem-solving skills.
• Good communication skills, both written and verbal.
• Ability to work collaboratively in a multidisciplinary team.
• Attention to detail and a commitment to producing high-quality work.

Required Technical Skills

• Proficiency in Python programming language.
• Strong understanding of computer vision concepts, including image processing, segmentation, and feature extraction. OR
• Demonstrated experience processing xCR data. OR
• Experience processing RNA sequencing data.
• Experience with libraries such as OpenCV, scikit-image, and PyTorch. (PyTorch is a main framework used by division).
• Familiarity with image analysis techniques used in biological and medical research.
• Knowledge of machine learning algorithms and their application to data.
• Ability to handle large datasets and perform statistical analysis.
• Proficiency in using data visualization tools such as Matplotlib, Seaborn, or similar.
• Understanding of LLM principles of work and experience in LLM integration with API OR
• Understanding of stable diffusion principles of work. Experience with multimodal models.