Our client in the Pharmaceutical Manufacturing industry is seeking a Principal Computer Vision Specialist to lead the development of multiple foundation models using biological imaging data, with the goal of accelerating target and biomarker discovery. This team is building a more integrated AI framework for early-stage research, using cellular imaging as a key modality to power a multi-modal foundation model for their in-vitro, high-throughput screening platform. In this role, you’ll be at the forefront of integrating generative AI into Research & Early Discovery, helping reduce the time from target identification to clinical application.
Day to Day:
* Lead the development and deployment of next-generation AI/ML models using cellular imaging and other biological data types (e.g., molecular, transcriptomics, biomedical literature).
* Define the strategy for applying generative AI in early-stage drug discovery, collaborating with cross-functional teams across biology, chemistry, and data science.
* Stay current with the latest research in computer vision, deep learning, representation learning, and multi-modal data integration.
* Communicate findings through reports, presentations, and scientific publications to both internal and external stakeholders.
* Build and maintain collaborations with academic institutions and industry partners.
Must Haves:
* PhD in Computer Science, Bioinformatics, Computational Biology, Physics, or a related field.
* High level of enterprise experience working in Computer Vision and SME experience in three key fields:
* Foundation models
* Self-supervised learning
* Vision transformers.
* Professional hands-on experience with pretraining or fine-tuning foundation models for computer vision tasks.
* Proven expertise in multi-modal data integration and representation learning, ideally applied to biological or pharmaceutical problems.
* Advanced programming skills in Python, with SME knowledge of deep learning frameworks in PyTorch, Hugging Face, or PyTorch Lightning.
* Proficiency in modern software development practices: version control (Git), continuous integration, testing, and Python packaging (e.g., uv).
Plusses:
* Experience with high-content screening, high-throughput data generation, or single-cell RNA sequencing.
* Familiarity with cloud computing platforms (e.g., AWS, Azure, Nvidia DGX Cloud) for large-scale model training and deployment.
* Knowledge of systems biology, biophysics, or causal inference in computational biology.
* Publication record in top-tier ML/CV conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML).