Computer Vision and Image Analysis Engineer

1.

Computer Vision and Image Analysis Engineer

This role combines cutting-edge AI with domain-specific challenges in biomedical imaging, offering you the opportunity to work closely with interdisciplinary teams of engineers, scientists, and healthcare professionals.

2.

Algorithm & Model Development

• Design, develop, and optimize state-of-the-art AI/computer vision models for a wide range of retinal imaging modalities (e.g., OCT, FAF, fundus, angiography, ultra-widefield, etc.).
• Build and refine deep learning-based solutions for disease diagnosis, clinical grading, disease activity monitoring, efficacy assessment and progression modeling across multiple retinal diseases (e.g. diabetic retinopathy, AMD, glaucoma).
• Develop and validate algorithms for image classification, segmentation, object detection and quantitative feature extraction, including biomarkers relevant to clinical trial endpoints.
• Create methods for image enhancement, transformation, denoising and pre/post-processing to support reliable downstream analysis in clinical trial settings.

Research & Innovation

• Stay up-to-date with cutting-edge advancements in AI, ophthalmic imaging and clinical trial methodologies, and translate these into novel solutions that meet regulatory and scientific requirements.
• Develop research and product roadmaps that align technology innovation with clinical development strategies and business objectives.

Clinical Trial Support & Implementation

• Design and implement AI tools to support clinical trials, including data ingestion and harmonization, automated analysis of imaging datasets for trial endpoints, and generation of quantitative metrics for efficacy assessments.
• Work with clinical teams and ophthalmologists to define clinically relevant outputs and ensure models meet the needs of protocol-defined objectives and regulatory standards.
• Contribute to the development of validation plans, clinical testing strategies, and documentation for regulatory submission of AI-based tools.

System Integration & Deployment

• Integrate AI/computer vision solutions into clinical and cloud-based platforms using Docker, Kubernetes and SaaS infrastructures, with specific focus on scalability, security and traceability for clinical trial applications.
• Collaborate with system vendors and stakeholders for implementation, testing and deployment of validated AI tools in regulated environments.

Collaboration & Communication

• Work closely with ophthalmologists, medical affairs, biostatisticians and machine learning engineers to translate clinical and scientific requirements into scalable, deployable technical solutions.
• Effectively communicate results, research findings and technical roadmaps to both scientific and non-technical stakeholders, including clinical development teams.

3.

Qualifications:

• Master’s degree in Computer Science, AI, Biomedical Engineering, or a related field.
• 5+ years of hands-on experience in computer vision, image analysis, and machine learning in healthcare or life sciences.
• Proven ability to independently lead AI research and model development.

Technical Skills :

I.

• Languages:

  Python (primary), C++, Java, R

• Libraries/Frameworks:

  OpenCV, TensorFlow, PyTorch, scikit-learn, Keras

II.

• Deep understanding of ML algorithms: CNNs, Vision Transformers, GANs, RNNs, SVMs, Random Forests, XGBoost
• Expertise in model optimization and deployment using

  multi-GPU systems

  ,

  Docker

  , and

  Kubernetes

III.

• Image classification (VGG, ResNet, EfficientNet)
• Object detection (YOLO, SSD, Faster R-CNN)
• Image segmentation (U-Net, Mask R-CNN)
• Feature extraction (SIFT, SURF, ORB)
• Image enhancement and transformation methods

IV.

• Familiarity with cloud platforms (AWS, Azure, GCP)
• Experience with SaaS, containerization, and high-performance computing for large-scale data

V.

• Ophthalmic imaging and diseases (e.g., fundus, OCT, glaucoma, diabetic retinopathy)
• Biomedical imaging in areas like cancer, neuroscience, immunology, or cardiovascular research
• Drug discovery and clinical trial workflows

VI.

• Strong problem-solving and analytical thinking
• Excellent communication skills (technical and non-technical audiences)
• High attention to detail with large, unstructured datasets
• Independent, self-driven work ethic with collaborative mindset

4.

• On-site role with occasional travel (up to 10%)
• Office-based work environment
• This is a largely sedentary position, requiring long periods of screen time