Deep Learning Engineer

Position: Senior Deep Learning Scientist

Location: Gothenburg

Mandatory: 3 days working from Gothenburg office is mandatory.

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.