Principal Computer Vision Scientist

Job Description

Our client in the Pharmaceutical Manufacturing industry is seeking a Principal Computer Vision Scientist to lead the development of 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- 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.
• Professional hands-on experience with pretraining or fine-tuning foundation models for computer vision tasks.
• Strong publication record in top-tier ML/CV conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML).
• 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).
• Subject matter expertise in foundation models, self-supervised learning, and vision transformers.

Plusses:

• Experience with high-content screening, high-throughput data , 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.
• Ability to write well-tested, well-documented code following best practices in machine learning and software engineering.

#J-18808-Ljbffr