AI Analytics Engineer

YD Talent Solutions; California; On-site; USD 140,000 - 180,000; Full time

Job summary

YDTS Global in Santa Clara, CA, seeks a Computer Vision Analytics Engineer to advance medical video analytics with real-time processing, edge-to-cloud pipelines, and AI-powered insights for endoscopic and MRI imaging.

You will collaborate with radiologists, deploy containerized models, and ensure regulatory compliance while optimizing low-latency inference for hospital environments.

Qualifications

• Experience in computer vision and AI model development, with medical imaging context.
• Ability to work with real-time video streams from clinical procedures.
• Understanding of HIPAA and medical device regulatory considerations.

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 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.

Skills

Computer vision

AI model development

Medical imaging knowledge

Team collaboration with clinicians

Edge-to-cloud processing

Tools

PyTorch

TensorFlow

OpenCV

Docker

Kubernetes

Job description

Santa Clara, United States | Posted on 03/24/2025

YDTS Global is led by seasoned talent and business leaders with decades of experience. We combine industry insight with practical execution to build your workforce advantage. At YDTS, our mission is to empower organizations through people. Founded by industry veterans, we blend global insights with hands-on experience to deliver tailored talent solutions. Unlike a traditional staffing agency, we integrate deeply as a long-term partner — advising on organization design, skill gaps, and culture fit while managing the end-to-end hiring process. Our leadership team has led GCC builds, scaled R&D centers, and placed hundreds of executives, and we bring that entrepreneurial and corporate DNA to every engagement. Our clients choose YDTS because we deliver more than candidates — we deliver solutions that transform organizations. We act as strategic advisors, aligning talent planning with business strategy, and as execution experts, deploying the right recruiting resources at the right time. We have scaled teams for Fortune 500 companies and emerging tech innovators alike.

Our consultants and recruiters bring domain knowledge to the table. YDTS has staffed teams and leaders in critical sectors: Technology & Semiconductors (e.g. firmware and AI research engineers); Life Sciences & Pharma/CRO (e.g. biologists, regulatory experts); Automotive & Aerospace (e.g. systems engineers, avionics talent); Manufacturing & Industrial (e.g. process engineers, plant managers); and Retail/FMCG (e.g. supply chain, product leads). For each sector, we tailor our approach. In life sciences, for example, compliance and specialized skills are paramount, so we leverage niche networks and universities. In tech industries, speed and culture-fit are critical, so we focus on coding simulations and cultural alignment. By organizing content by industry, visitors quickly see that we “speak their language” and have relevant success stories.

Job Description

Computer Vision Analytics Engineer – Medical Video/Image Analytics

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.

Requirements

Qualifications:

• Demonstrated experience in computer vision, AI model development, and optimization.
• Experience working with medical videos, including MRI, endoscopy, ultrasound, echo-cardiograms, and OCR-based recognition.
• Proficiency in multi-modal 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.
• 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.

YDTS Global is an Equal Opportunity Employer and does not discriminate on the basis of race or ethnicity, religion, sex, national origin, age, veteran disability or genetic information or any other reason prohibited by law in employment.