What are the responsibilities and job description for the Computer Vision Analytics Engineer – Medical Video / Image Analytics position at Themesoft Inc.?

Computer Vision Analytics Engineer – Medical Video / Image Analytics

Job Title : Computer Vision Analytics Engineer – Medical Video / Image Analytics

Location : SFO / Santa Clara, CA – Onsite – 3-4 days a week in the office.

Number of openings : 2

Job Description :

We are seeking Computer Vision Analytics Engineers to support a Medical Video Analytics Project. This initiative integrates real-time medical video processing, AI-powered computer vision, and cloud-based analytics to enhance endoscopic procedures and MRI imaging.

The role involves working on edge-to-cloud video processing pipelines, developing vision algorithms for real-time object detection, and building machine learning models that generate automated insights and recommendations for medical professionals.

Key Responsibilities :

Work with real-time video feeds from robotic-assisted surgery and endoscopic procedures.
Support remote and in-hospital control workflows for AI-enhanced video analytics.
Process and analyze high-speed medical video streams at gigabit-per-second (Gbps) throughput.
Ensure secure transmission of MRI and endoscopic video feeds from edge devices to the cloud.
Develop scalable Edge-to-Cloud AI solutions, ensuring low-latency inference for various medical applications.
Implement AI models that analyze video content and classify frames as useful or non-useful.
Develop AI-driven video segmentation and classification models to filter relevant vs. non-relevant frames.
Develop object detection, segmentation, and tracking models to identify anatomical structures, surgical instruments, and procedural steps in real time.
Implement video enhancement and denoising techniques to improve image clarity and feature extraction.
Deploy deep learning-based models for medical video analytics using TensorFlow, PyTorch, and OpenCV.
Compare real-time footage with pre-trained medical video datasets to generate automated insights.
Develop containerized AI models (Docker, Kubernetes) to ensure scalable deployment in hospital environments.
Integrate AI-powered video analytics pipelines with cloud-based AI models (e.g., Azure AI).
Ensure seamless bi-directional communication between cloud AI models and edge computing systems.
Work closely with radiologists and healthcare professionals to fine-tune AI-driven video object detection and recommendations.
Integrate AI-powered video analytics solutions with existing hospital PACS, DICOM storage, and medical imaging infrastructure.
Ensure AI models comply with HIPAA, FDA, and medical device regulations for clinical deployment.

Qualifications :

Demonstrated experience in computer vision, AI model development, and optimization.

Experience working with medical videos, including MRI, endoscopy, ultrasound, echocardiograms, and OCR-based recognition.

Proficiency in multimodal AI, integrating various medical imaging sources.

Experience working closely with healthcare professionals and hospital workflows.

Experience integrating AI models with hospital IT systems, PACS, and DICOM-based workflows.

Proficiency in Python and experience with AI frameworks such as PyTorch, TensorFlow, OpenCV.

Expertise in computer vision techniques, including Object detection (YOLO, SSD, Faster R-CNN), Image segmentation (U-Net, Mask R-CNN), Image classification (ResNet, EfficientNet, ViTs), Feature extraction (SIFT, SURF, ORB).

Strong knowledge of machine learning techniques including supervised, unsupervised, and self-supervised learning, CNNs, Vision Transformers (ViTs), GANs, attention-based networks, Random forests, SVMs, boosting algorithms.

Proficiency in data preprocessing, augmentation, normalization, and handling large-scale image datasets.

Experience working with multi-GPU workloads for training and inference.

Experience deploying models using containerization technologies (Docker, Kubernetes).

Experience with high-performance computing (HPC) techniques for managing large-scale datasets.

Background in federated learning for medical AI to enhance privacy-preserving model training.

Prior experience in developing AI solutions for real-time clinical applications.

Strong understanding of regulatory constraints in AI-driven medical applications.

Ability to effectively communicate complex AI models to technical and non-technical stakeholders.

Seniority level : Mid-Senior level

Employment type : Contract

Job function : Information Technology

Industries : IT Services and IT Consulting

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Computer Vision Analytics Engineer – Medical Video / Image Analytics

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