Sr. Applied Researcher - Computer Vision

Senior Applied Researcher - Computer Vision

We're seeking a senior computer vision and deep learning expert to join our applied algorithm team in developing AI solutions for medical image analysis. You will design, implement, and optimize machine learning models for interpreting data captured by our proprietary imaging platform, with application in blood count analysis. Your work will include research and development towards production, incorporating SOTA techniques into robust and accurate solutions deployed in clinical settings..

Responsibilities

• Design and implement machine learning algorithms for analyzing medical video images captured by our proprietary spectroscopy-based microscope.
• Research and integrate state-of-the-art methods in computer vision and image analysis (both deep and classical methods) to enhance the accuracy and efficiency of ML models.
• Review, reproduce, and apply findings from relevant scientific literature to improve algorithmic performance.
• Develop and maintain scalable data processing pipelines and model deployment workflows.
• Analyze large-scale clinical trial data to support the development and validation of predictive blood count models.
• Collaborate closely with physicists, software engineers and HW engineers in a fast-paced, multidisciplinary environment.

Qualifications

• MSc or PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Applied Mathematics, or a related field.
• A top performer with at least 7 years of hands-on, post-academic experience in developing deep learning and computer vision algorithms.
• Proven ability to lead algorithm development from ideation through implementation, including experimental design, iterative development, and performance evaluation.
• Strong expertise in modern deep learning frameworks such as PyTorch and numerical libraries such as numpy and scikit-learn.
• Excellent problem-solving skills, critical thinking, and the ability to work both independently and collaboratively in a multidisciplinary team.
• Experience working with medical or biological data is an advantage.