Deep Learning Engineer

Role Overview As a Senior Deep Learning Scientist, you will play a pivotal role in developing and implementing advanced models for multimodality data to predict the toxicity of compounds, ultimately impacting patient outcomes. You will leverage cutting-edge deep learning techniques and work on a range of responsibilities to ensure the effectiveness and accuracy of the models. Key Responsibilities Perform in-depth exploratory data analysis for both image and tabular datasets. Conduct feature engineering to enhance model performance. Implement augmentation techniques to enhance the robustness of model. Handle batch correction for both image and tabular data to improve model performance.

Handle multi-dimensional image data and develop segmentation pipelines to extract individual cells from images. Build deep learning models capable of predicting toxicity with confidence intervals. Collaborate with cross-functional teams to integrate deep learning models into existing workflows. Develop and maintain documentation for model processes and findings. Present research findings and updates to stakeholders. Optimize computational resources and improve model efficiency. Deploy models in platform of interest. Preferred Experience Prior experience with Contrastive learning approaches such as DINO, SimCLR, BYOL for images. Familiarity with transformer-based architectures for tabular data, such as TabTransformer.

Proficiency in PyTorch Lightning and PyTorch for model development and training. Hands-on experience in designing, training, and deploying advanced deep learning models. Theoretical understanding of deep computer vision models, including image classification, segmentation, multi-modality models, diffusion models, and large language models. PhD in machine learning, computer science, math, or physics. Established track record of publications in top-tier conferences or journals. Hands-on experience in training, debugging, and deploying deep learning models. Proficient coding skills in machine learning products, not limited to research prototypes. Motivated to stay updated with the latest research in deep learning and contribute to product improvement.

Strong problem-solving skills and the ability to work independently. Excellent communication skills to effectively convey technical concepts to non-technical stakeholders.