Machine Learning Engineer

• Processing raw data, leverage Python to train AI models (including CNNs, Transformer models, GNNs, etc.), and rigorously validate the results
• Manage databases efficiently and deploy AI models for on-demand usage
• As demand for our solutions grows, you'll contribute to scaling hardware resources to meet the increasing customer needs
• Work closely with our computer software engineering team to improve and optimize our digital pathology viewer
• Remain up to date with the latest advancements in machine learning and maintain a strong understanding of state-of-the-art ML practices to ensure our solutions remain cutting-edge and effective
• Design & Development: Design, develop, and implement robust machine learning systems and AI software, primarily in Computer Vision
• Model Building & Evaluation: Perform data preprocessing, feature engineering, and train/evaluate ML models to ensure optimal performance
• Deployment & Integration: Integrate ML models into production systems and workflows, working with data pipelines and cloud/bare-metal platforms
• Collaboration: Partner with data scientists, product managers, and software engineers to define project objectives and deliver business goals
• Optimization & Troubleshooting: Analyze results, troubleshoot deployed models, and implement changes to improve efficiency and scalability
• Innovation: Document the development process while staying current with the latest advancements in AI, ML, and computer vision

• 2+ years in similar fulltime ML role OR PhD/Master’s degree in related field (computer vision or ML in bioinformatics) with 2+ publications at top-tier conferences/journals
• Proven ability to manage multiple roles and move fast without supervision
• Expert proficiency in Python and strong knowledge of software development best practices (unit testing, source control)
• In-depth knowledge of PyTorch, TensorFlow, scikit-learn, NumPy, OpenCV, OpenSlide, and pandas
• Core domain-specific experience: Computer Vision, Vision Transformers, CNNs
• Expert knowledge of Git/GitHub, specifically managing complex repositories and utilizing GitHub Actions/Secrets
• Bachelor’s or Master’s degree in CS, Statistics, Mathematics, Engineering, or a related quantitative field

• Experience in pathology-libraries would be an asset
• Advantage to have experience with any of the following: Stable diffusion, Conditional GANS, Pix2pix, computer vision foundation models, GNNs, CGNs, model distillation
• Experience in histology/pathology would be an asset
• Driven by Quality: attention to detail, learning and understanding new concepts, and a strong dedication to quality
• Experience with Docker for containerization and deployment
• Prior experience: work in life science, biopharma, or health care
• We highly value candidates with healthcare AI experience
• Familiarity with medical imaging and pathology is a significant advantage
• Strong leadership qualities for guiding teams, project management skills, a willingness to adapt and learn, and effective communication across diverse backgrounds are qualities that will truly stand out
• Good code documentation practice and organization is also highly desired

equity/ stock options