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I am Mehdi Hajoub, a Machine Learning & Image Analysis Engineer focused on medical imaging, model development, and statistical quality analysis. I apply Python, computer vision, and quantitative methods to evaluate the stability and reliability of biological and clinical data. I have a track record deploying robust, reproducible pipelines in healthcare environments (CHUV, Campus Biotech) and am passionate about using AI to support diagnostic workflows, including segmentation and classification, stain monitoring, and imaging QC.…I am Mehdi Hajoub, a Machine Learning & Image Analysis Engineer focused on medical imaging, model development, and statistical quality analysis. I apply Python, computer vision, and quantitative methods to evaluate the stability and reliability of biological and clinical data. I have a track record deploying robust, reproducible pipelines in healthcare environments (CHUV, Campus Biotech) and am passionate about using AI to support diagnostic workflows, including segmentation and classification, stain monitoring, and imaging QC.

MEHDI HAJOUB

AI Engineer, AI Developer, AI Prompt Engineer, +7

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I am Mehdi Hajoub, a Machine Learning & Image Analysis Engineer focused on medical imaging, model development, and statistical quality analysis. I apply Python, computer vision, and quantitative methods to evaluate the stability and reliability of biological and clinical data. I have a track record deploying robust, reproducible pipelines in healthcare environments (CHUV, Campus Biotech) and am passionate about using AI to support diagnostic workflows, including segmentation and classification, stain monitoring, and imaging QC.…I am Mehdi Hajoub, a Machine Learning & Image Analysis Engineer focused on medical imaging, model development, and statistical quality analysis. I apply Python, computer vision, and quantitative methods to evaluate the stability and reliability of biological and clinical data. I have a track record deploying robust, reproducible pipelines in healthcare environments (CHUV, Campus Biotech) and am passionate about using AI to support diagnostic workflows, including segmentation and classification, stain monitoring, and imaging QC.

Available to hire

I am Mehdi Hajoub, a Machine Learning & Image Analysis Engineer focused on medical imaging, model development, and statistical quality analysis. I apply Python, computer vision, and quantitative methods to evaluate the stability and reliability of biological and clinical data.

I have a track record deploying robust, reproducible pipelines in healthcare environments (CHUV, Campus Biotech) and am passionate about using AI to support diagnostic workflows, including segmentation and classification, stain monitoring, and imaging QC.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

French

Fluent

English

Fluent

German

Intermediate

Italian

Intermediate

Work Experience

Machine Learning Engineer Intern at Campus Biotech

January 1, 2025 - November 24, 2025

Deployed graph neural network system for neurodegenerative disease classification (12 disorders, 600+ fMRI scans) serving 15+ researchers with 99.2% uptime and sub-2-second inference latency. Built automated retraining pipeline with Airflow and Docker, reducing update cycle from quarterly to weekly and preprocessing time from 72h to 4h through stability-based feature selection (60% storage reduction).

AI Project Developer at EPFL AI Center

May 1, 2024 - May 1, 2024

Developed real-time sign language recognition system (1K videos, 50K frames) using Temporal Transformer architecture with CTC decoder, achieving 91% accuracy at 30 FPS. Compressed model from 42M to 11M parameters via pruning and quantization while maintaining 88% accuracy with 3× faster inference, enabling mobile deployment.

Machine Learning Researcher at EPFL LTS5 Lab

January 1, 2024 - January 1, 2024

Trained medical imaging models (ResNet-50, EfficientNet-B3) for cervical cancer detection on 200-image dataset, achieving 93% accuracy with cross-hospital validation (Geneva ↔ Cameroon). Improved domain generalization by 18% and reduced critical false negatives by 22% through adversarial domain adaptation and hybrid supervision techniques.

Machine Learning Engineer Intern at Campus Biotech, Geneva

January 1, 2025 - November 25, 2025

Deployed graph neural network system for neurodegenerative disease classification (12 disorders, 600+ fMRI scans) serving 15+ researchers with 99.2% uptime and sub-2-second inference latency; built automated retraining pipeline with Airflow and Docker, reducing update cycle from quarterly to weekly and preprocessing time from 72h to 4h through stability-based feature selection (60% storage reduction).

Machine Learning Engineer Intern at NeuroRestore (CHUV-EPFL), Lausanne

March 1, 2024 - March 1, 2024

Built multimodal NLP system to objectively quantify rehabilitation progress in spinal cord injury patients: automated transcription of 40+ hours of therapy sessions with medical language embeddings, synchronized to neuro-stimulation parameters and EMG data to measure movement quality and correlate verbal commands with motor recovery; optimized production inference by 65% (8.2s → 2.9s) through INT8 quantization and batch processing, enabling near-real-time clinical feedback; maintained deployment with Docker containers and CI/CD pipeline (GitHub Actions).

AI Project Developer at EPFL AI Center, Lausanne

May 1, 2024 - May 1, 2024

Developed real-time sign language recognition system (1K videos, 50K frames) using Temporal Transformer architecture with CTC decoder, achieving 91% accuracy at 30 FPS; compressed model from 42M to 11M parameters via pruning and quantization while maintaining 88% accuracy with 3× faster inference, enabling mobile deployment.

Machine Learning Researcher at EPFL LTS5 Lab, Lausanne

January 1, 2024 - January 1, 2024

Trained medical imaging models (ResNet-50, EfficientNet-B3) for cervical cancer detection on a 200-image dataset, achieving 93% accuracy with cross-hospital validation (Geneva ↔ Cameroon); improved domain generalization by 18% and reduced critical false negatives by 22% through adversarial domain adaptation and hybrid supervision techniques.

Clinical ML & Data Pipeline Engineer at NeuroRestore (CHUV-EPFL), Lausanne

September 1, 2024 - Present

Built multimodal NLP system to objectively quantify rehabilitation progress in spinal cord injury patients: automated transcription of 40+ hours of therapy sessions with medical language embeddings, synchronized to neuro-stimulation parameters and EMG data to measure movement quality and correlate verbal commands with motor recovery; deployed production-grade clinical software integrating the full NLP pipeline; containerized services with optimized inference (INT8 quantization, batched processing) reducing end-to-end latency from 8.2s to 2.9s and enabling near–real-time use by therapists

Medical Image Analysis Researcher at EPFL LTS5 Lab, Lausanne

September 1, 2023 - January 1, 2024

Trained medical imaging models (ResNet-50, EfficientNet-B3) for cervical cancer detection on 200-image dataset, achieving 93% accuracy with cross-hospital validation (Geneva ↔ Cameroon); improved domain generalization by 18% and reduced critical false negatives by 22% through adversarial domain adaptation and hybrid supervision techniques