Open to opportunities

Anael Leinert

@anaelleinert

I build efficient computer-vision models for real-world deployment.

At Jolibrain, I manage large video datasets, mine data, and iteratively train flare-detection models for image data. I also deploy and maintain a preventive-maintenance web application for electric lines on the French national railway network.

Previously at ams OSRAM, I collected data and trained compute-efficient person-detection models from low-resolution DTOF depth sensors, deploying them with NNOM. At DIPIX Systems, I trained and deployed deep models for 6D object pose estimation, generated synthetic Blender training data, and addressed domain gap and mobile deployment efficiency.

My research experience spans deep generative models for 3D scenes, medical data science in oncology, and sensor fusion for 6D pose estimation. I work across C, C++, Python, PyTorch, TensorFlow, ONNX, and computer-vision tooling.

Experience

Work history, roles, and key accomplishments

Machine Learning Engineer

Jolibrain

Oct 2024 - Dec 2025 (1 year 2 months)

Managed big video datasets and iteratively trained flare detection models on image data. Deployed and maintained a web application for preventive maintenance of electric lines on the French national railway network.

C++Python Data Mining Machine Learning

Machine Learning Engineer

ams OSRAM

Jul 2022 - Dec 2023 (1 year 5 months)

Collected data and trained compute-efficient models for person detection from low-resolution DTOF depth sensors. Deployed models using the NNOM library.

Python Docker PyTorch TensorFlow scikit learn Pandas OpenCV Git Github Onnx

Machine Learning Engineer

DIPIX Systems

Sep 2020 - Jan 2022 (1 year 4 months)

Trained and deployed deep models for 6D pose estimation of objects. Developed scripts for synthetic training data generation using Blender and created real-world pose estimation datasets with ArUco markers.

Python TensorFlow PyTorch scikit learn Pandas OpenCV Blender Onnx Git Github

ML Research Intern

GIPSA-lab

Jan 2019 - Jun 2019 (5 months)

Experimented with deep generative models for 3D scene encoding and rendering, including Generative Query Networks and variational autoencoders. Created datasets using Blender scripting.

Deep Learning Generative Models Variational Auto Encoders Blender Python

Medical Data Science Research Intern

A2 INSERM

Jan 2018 - Jun 2018 (5 months)

Designed experiments for diagnosis and prognosis in oncology. Compared robustness of statistical metrics from radiology and MR images of cancer tissue, focusing on acquisition methods.

Data Science Statistics Medical Imaging

Research Intern

TIMC-IMAG

Sep 2016 - Jan 2017 (4 months)

Worked on sensor fusion for 6D pose estimation of a fake echographic probe for an echography simulator. Designed experiments to evaluate robustness of sensors including accelerometer, gyroscope, and Leap Motion.

C C++Sensor Fusion Pose Estimation