Job Description

Are you an Imaging Expert? Want to help revolutionalise Drug Discovery? Want to be part of the AI revolution? 

Job Title : Computational Imaging Scientist – AI and Toxicology Prediction
Location : San Francisco, Bay Area

Company Overview :
I am working with a cutting-edge company revolutionizing toxicology through the power of AI and computational imaging.

Their mission is to replace traditional chemical toxicity testing with AI-driven predictive model s that enhance safety in the pharmaceutical, food, cosmetics, and agricultural sectors.

Leveraging a unique combination of high-content microscopy, chemical data, and advanced neural network architectures , we are building pioneering models that translate chemical structures into biological responses, offering an innovative, scalable approach to toxicity prediction .

Position Summary :
Looking to ID an expert in computational imaging with a strong background in AI and machine learning to join our team.

In this role, you will spearhead the development of high-content imaging pipelines , integrating primary cell microscopy data with neural network-based model s to generate insights into chemical toxicity.

This is a unique opportunity to contribute to a transformative field, where your expertise will be pivotal in creating AI models capable of rivaling and surpassing traditional physical experiments. 

Key Responsibilities :

1. Microscopy and Image Data Generation :
   • Develop and oversee high-content imaging workflows, generating extensive microscopy datasets on primary cells.
   • Optimize imaging modalities for accuracy and detail, facilitating downstream integration with chemical and biochemical data.
   • Lead initiatives to generate over 40 million cell images across a diverse set of 50,000 small molecules at various concentration levels.
2. Machine Learning Model Development :
   • Design, train, and refine computer vision models, including Convolutional Neural Networks (CNNs) and Vision Transformers, to classify images, detect phenotypic features, and perform anomaly detection.
   • Develop and apply image and text encoder models to enable text-to-image predictions, enhancing the ability to predict chemical-biology interactions.
   • Innovate in multi-modal model development to create a "chemical-to-biology" predictive model, mapping chemical structures to biological phenotypes.
3. High-Throughput Phenotypic Analysis :
   • Develop algorithms to detect and quantify phenotypes in high-content imaging, capturing the "phenotypic landscape" and identifying biologically diverse responses.
   • Build and validate machine learning models that utilize high-resolution imaging data to accurately predict human toxicity profiles.
4. AI and Neural Network Research :
   • Conduct research on novel neural network architectures for imaging applications, focusing on deep learning models, transformer models, and large-scale pretraining.
   • Collaborate with the data science and AI teams to integrate imaging data into large language models (LLMs) and multi-modal representations for in-depth data exploration.
5. Collaboration and Innovation :
   • Work cross-functionally with chemists, biologists, and toxicologists to align imaging processes with broader project goals.
   • Actively contribute to a collaborative research environment, presenting findings and publishing research to advance the field of AI in toxicology.
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Qualifications :

• Education : PhD or Master’s degree in Computational Imaging, Computer Vision, Biomedical Engineering, or a related field.
• Experience : You would consider yourself an expert  in imaging science, with a strong background in high-content microscopy, cell imaging, and computational biology.
• Technical Expertise :
  • Proficiency in Python and relevant machine learning libraries (e.g., PyTorch, TensorFlow).
  • Extensive experience with Convolutional Neural Networks ( CNNs ), Vision Transformers , and other deep learning architectures for imaging modalities.
  • Background in multi-modal model development, integrating imaging data with structured data (e.g., chemical or biochemical datasets).
  • Expertise in anomaly detection, image classification, and other computer vision techniques.
• Domain Knowledge : Familiarity with chemical and biological data and experience applying imaging techniques to biochemical and toxicological contexts.

Preferred Skills :

• Experience working with large-scale imaging datasets and high-throughput imaging systems.
• Knowledge of phenotypic profiling, feature extraction, and image-to-structure prediction models.
• Demonstrated contributions to AI-driven scientific research, including published papers and/or patents in the field.

What We Offer :

• Competitive salary and benefits package.
• Opportunity to work at the forefront of AI and imaging science with a team of world-class experts.
• Collaborative work environment dedicated to pushing the boundaries of scientific discovery.

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